var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"jsonp\":\"1\",\"host\":\"bibbase.org\"},\n      data: [{\"title\":\"Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields\",\"type\":\"article\",\"year\":\"2018\",\"websites\":\"http://arxiv.org/abs/1806.07787\",\"month\":\"6\",\"day\":\"20\",\"id\":\"26e73a13-c65f-31fc-9a3d-25edbdb49420\",\"created\":\"2021-04-20T12:48:04.692Z\",\"accessed\":\"2021-04-20\",\"file_attached\":\"true\",\"profile_id\":\"1bff199d-3fb6-39f8-9a95-8103f3a5d433\",\"group_id\":\"1619b2da-6d2b-31f6-b805-355bf28ff212\",\"last_modified\":\"2021-04-20T12:48:06.831Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"In this paper, the main goal is to detect a movie reviewer's opinion using hidden conditional random fields. 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Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translation (NMT) to improve the modelling of tag sequentiality. Seq2seq models are known to learn complex global dependencies while currently proposed approaches using linear conditional random fields (CRF) only model local tag dependencies. In this work, we introduce a seq2seq model tailored for DA classification using: a hierarchical encoder, a novel guided attention mechanism and beam search applied to both training and inference. Compared to the state of the art our model does not require handcrafted features and is trained end-to-end. Furthermore, the proposed approach achieves an unmatched accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on MRDA.</p>\",\"bibtype\":\"article\",\"author\":\"Colombo, Pierre and Chapuis, Emile and Manica, Matteo and Vignon, Emmanuel and Varni, Giovanna and Clavel, Chloe\",\"doi\":\"10.1609/aaai.v34i05.6259\",\"journal\":\"Proceedings of the AAAI Conference on Artificial Intelligence\",\"number\":\"05\",\"bibtex\":\"@article{\\n title = {Guiding Attention in Sequence-to-Sequence Models for Dialogue Act Prediction},\\n type = {article},\\n year = {2020},\\n volume = {34},\\n month = {4},\\n day = {3},\\n id = {157703f0-fe46-3486-afab-9f9f3ac97356},\\n created = {2021-04-20T12:48:05.251Z},\\n file_attached = {false},\\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\\n group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},\\n last_modified = {2021-04-20T12:48:05.251Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {<p>The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. 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We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.},\\n bibtype = {article},\\n author = {Chapuis, Emile and Colombo, Pierre and Manica, Matteo and Labeau, Matthieu and Clavel, Chloe}\\n}\",\"author_short\":[\"Chapuis,  E.\",\"Colombo,  P.\",\"Manica,  M.\",\"Labeau,  M.\",\"Clavel,  C.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/09f0fed5-76a2-83f3-7121-10654c99f1b5/full_text.pdf.pdf\",\"Website\":\"http://arxiv.org/abs/2009.11152\"},\"biburl\":\"https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433\",\"bibbaseid\":\"chapuis-colombo-manica-labeau-clavel-hierarchicalpretrainingforsequencelabellinginspokendialog-2020\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{\"colombo, p\":\"https://pierrecolombo.github.io/\",\"clavel,  c\":\"https://maoiproject.github.io/publications/\"}},\"downloads\":3,\"html\":\"\"},{\"title\":\"Adapting sentiment analysis to face-to-face human-agent interactions: From the detection to the evaluation issues\",\"type\":\"article\",\"year\":\"2015\",\"keywords\":\"alignment,embodied conversational agent,emotional stance,engagement,other-repetition\",\"pages\":\"14-20\",\"publisher\":\"IEEE\",\"id\":\"92200a35-b123-3234-a284-c41d8d0da577\",\"created\":\"2021-04-20T12:48:06.227Z\",\"file_attached\":\"true\",\"profile_id\":\"1bff199d-3fb6-39f8-9a95-8103f3a5d433\",\"group_id\":\"1619b2da-6d2b-31f6-b805-355bf28ff212\",\"last_modified\":\"2021-04-20T12:48:07.714Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"© 2015 IEEE. This paper introduces a sentiment analysis method suitable to the human-agent and face-to-face interactions. We present the positioning of our system and its evaluation protocol according to the existing sentiment analysis literature and detail how the proposed system integrates the human-agent interaction issues. Finally, we provide an in-depth analysis of the results obtained by the evaluation, opening the discussion on the different difficulties and the remaining challenges of sentiment analysis in human-agent interactions.\",\"bibtype\":\"article\",\"author\":\"Langlet, Caroline and Clavel, Chloe\",\"doi\":\"10.1109/ACII.2015.7344545\",\"journal\":\"2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015\",\"bibtex\":\"@article{\\n title = {Adapting sentiment analysis to face-to-face human-agent interactions: From the detection to the evaluation issues},\\n type = {article},\\n year = {2015},\\n keywords = {alignment,embodied conversational agent,emotional stance,engagement,other-repetition},\\n pages = {14-20},\\n publisher = {IEEE},\\n id = {92200a35-b123-3234-a284-c41d8d0da577},\\n created = {2021-04-20T12:48:06.227Z},\\n file_attached = {true},\\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\\n group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},\\n last_modified = {2021-04-20T12:48:07.714Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {© 2015 IEEE. This paper introduces a sentiment analysis method suitable to the human-agent and face-to-face interactions. We present the positioning of our system and its evaluation protocol according to the existing sentiment analysis literature and detail how the proposed system integrates the human-agent interaction issues. Finally, we provide an in-depth analysis of the results obtained by the evaluation, opening the discussion on the different difficulties and the remaining challenges of sentiment analysis in human-agent interactions.},\\n bibtype = {article},\\n author = {Langlet, Caroline and Clavel, Chloe},\\n doi = {10.1109/ACII.2015.7344545},\\n journal = {2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015}\\n}\",\"author_short\":[\"Langlet,  C.\",\"Clavel,  C.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/111b973f-7cba-b2ff-203e-891ad8d9f665/07344545.pdf.pdf\"},\"biburl\":\"https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433\",\"bibbaseid\":\"langlet-clavel-adaptingsentimentanalysistofacetofacehumanagentinteractionsfromthedetectiontotheevaluationissues-2015\",\"role\":\"author\",\"keyword\":[\"alignment\",\"embodied conversational agent\",\"emotional stance\",\"engagement\",\"other-repetition\"],\"metadata\":{\"authorlinks\":{\"clavel,  c\":\"https://maoiproject.github.io/publications/\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue\",\"type\":\"article\",\"year\":\"2020\",\"websites\":\"https://arxiv.org/abs/2002.09419\",\"month\":\"2\",\"day\":\"21\",\"id\":\"eb1e9b02-d394-3697-b2ed-33a1e0d81d80\",\"created\":\"2021-04-20T12:48:52.944Z\",\"accessed\":\"2021-04-20\",\"file_attached\":\"true\",\"profile_id\":\"1bff199d-3fb6-39f8-9a95-8103f3a5d433\",\"group_id\":\"1619b2da-6d2b-31f6-b805-355bf28ff212\",\"last_modified\":\"2021-04-20T12:48:53.467Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"The task of predicting dialog acts (DA) based on conversational dialog is a\\nkey component in the development of conversational agents. 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Accurately\\npredicting DAs requires a precise modeling of both the conversation and the\\nglobal tag dependencies. We leverage seq2seq approaches widely adopted in\\nNeural Machine Translation (NMT) to improve the modelling of tag sequentiality.\\nSeq2seq models are known to learn complex global dependencies while currently\\nproposed approaches using linear conditional random fields (CRF) only model\\nlocal tag dependencies. In this work, we introduce a seq2seq model tailored for\\nDA classification using: a hierarchical encoder, a novel guided attention\\nmechanism and beam search applied to both training and inference. Compared to\\nthe state of the art our model does not require handcrafted features and is\\ntrained end-to-end. 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 title = {Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields},
 type = {article},
 year = {2018},
 websites = {http://arxiv.org/abs/1806.07787},
 month = {6},
 day = {20},
 id = {26e73a13-c65f-31fc-9a3d-25edbdb49420},
 created = {2021-04-20T12:48:04.692Z},
 accessed = {2021-04-20},
 file_attached = {true},
 profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},
 group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},
 last_modified = {2021-04-20T12:48:06.831Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, the main goal is to detect a movie reviewer's opinion using hidden conditional random fields. This model allows us to capture the dynamics of the reviewer's opinion in the transcripts of long unsegmented audio reviews that are analyzed by our system. High level linguistic features are computed at the level of inter-pausal segments. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the ICT-MMMO corpus. We obtain a F1-score of 82\%, which is better than logistic regression and recurrent neural network approaches. We also offer a discussion that sheds some light on the capacity of our system to adapt the word embedding model learned from general written texts data to spoken movie reviews and thus model the dynamics of the opinion.},
 bibtype = {article},
 author = {Barriere, Valentin and Clavel, Chloé and Essid, Slim}
}

@article{
 title = {Guiding Attention in Sequence-to-Sequence Models for Dialogue Act Prediction},
 type = {article},
 year = {2020},
 volume = {34},
 month = {4},
 day = {3},
 id = {157703f0-fe46-3486-afab-9f9f3ac97356},
 created = {2021-04-20T12:48:05.251Z},
 file_attached = {false},
 profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},
 group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},
 last_modified = {2021-04-20T12:48:05.251Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {<p>The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translation (NMT) to improve the modelling of tag sequentiality. Seq2seq models are known to learn complex global dependencies while currently proposed approaches using linear conditional random fields (CRF) only model local tag dependencies. In this work, we introduce a seq2seq model tailored for DA classification using: a hierarchical encoder, a novel guided attention mechanism and beam search applied to both training and inference. Compared to the state of the art our model does not require handcrafted features and is trained end-to-end. Furthermore, the proposed approach achieves an unmatched accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on MRDA.</p>},
 bibtype = {article},
 author = {Colombo, Pierre and Chapuis, Emile and Manica, Matteo and Vignon, Emmanuel and Varni, Giovanna and Clavel, Chloe},
 doi = {10.1609/aaai.v34i05.6259},
 journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
 number = {05}
}

@article{
 title = {Hierarchical Pre-training for Sequence Labelling in Spoken Dialog},
 type = {article},
 year = {2020},
 websites = {http://arxiv.org/abs/2009.11152},
 month = {9},
 day = {23},
 id = {8beb5535-2ca0-3d30-a16d-10cd26b5f67e},
 created = {2021-04-20T12:48:05.799Z},
 accessed = {2021-04-20},
 file_attached = {true},
 profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},
 group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},
 last_modified = {2021-04-20T12:48:07.281Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (\textttSILICONE). \textttSILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over $2.3$ billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.},
 bibtype = {article},
 author = {Chapuis, Emile and Colombo, Pierre and Manica, Matteo and Labeau, Matthieu and Clavel, Chloe}
}

@article{
 title = {Adapting sentiment analysis to face-to-face human-agent interactions: From the detection to the evaluation issues},
 type = {article},
 year = {2015},
 keywords = {alignment,embodied conversational agent,emotional stance,engagement,other-repetition},
 pages = {14-20},
 publisher = {IEEE},
 id = {92200a35-b123-3234-a284-c41d8d0da577},
 created = {2021-04-20T12:48:06.227Z},
 file_attached = {true},
 profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},
 group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},
 last_modified = {2021-04-20T12:48:07.714Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2015 IEEE. This paper introduces a sentiment analysis method suitable to the human-agent and face-to-face interactions. We present the positioning of our system and its evaluation protocol according to the existing sentiment analysis literature and detail how the proposed system integrates the human-agent interaction issues. Finally, we provide an in-depth analysis of the results obtained by the evaluation, opening the discussion on the different difficulties and the remaining challenges of sentiment analysis in human-agent interactions.},
 bibtype = {article},
 author = {Langlet, Caroline and Clavel, Chloe},
 doi = {10.1109/ACII.2015.7344545},
 journal = {2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015}
}

@article{
 title = {Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue},
 type = {article},
 year = {2020},
 websites = {https://arxiv.org/abs/2002.09419},
 month = {2},
 day = {21},
 id = {eb1e9b02-d394-3697-b2ed-33a1e0d81d80},
 created = {2021-04-20T12:48:52.944Z},
 accessed = {2021-04-20},
 file_attached = {true},
 profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},
 group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},
 last_modified = {2021-04-20T12:48:53.467Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {The task of predicting dialog acts (DA) based on conversational dialog is a
key component in the development of conversational agents. Accurately
predicting DAs requires a precise modeling of both the conversation and the
global tag dependencies. We leverage seq2seq approaches widely adopted in
Neural Machine Translation (NMT) to improve the modelling of tag sequentiality.
Seq2seq models are known to learn complex global dependencies while currently
proposed approaches using linear conditional random fields (CRF) only model
local tag dependencies. In this work, we introduce a seq2seq model tailored for
DA classification using: a hierarchical encoder, a novel guided attention
mechanism and beam search applied to both training and inference. Compared to
the state of the art our model does not require handcrafted features and is
trained end-to-end. Furthermore, the proposed approach achieves an unmatched
accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on
MRDA.},
 bibtype = {article},
 author = {Colombo, Pierre and Chapuis, Emile and Manica, Matteo and Vignon, Emmanuel and Varni, Giovanna and Clavel, Chloe}
}

@inproceedings{
 title = {HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection},
 type = {inproceedings},
 year = {2020},
 id = {3850cc8d-371e-3313-9d8d-1c6c0a58d228},
 created = {2021-04-22T16:44:17.606Z},
 file_attached = {false},
 profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},
 group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},
 last_modified = {2021-04-22T16:44:17.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 source_type = {inproceedings},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Atamna, Asma and Clavel, Chloé},
 booktitle = {INTERSPEECH 2020}
}\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! 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In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"colombo-chapuis-manica-vignon-varni-clavel-guidingattentioninsequencetosequencemodelsfordialogueactprediction-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Guiding Attention in Sequence-to-Sequence Models for Dialogue Act Prediction.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Colombo,  P.; Chapuis,  E.; Manica,  M.; Vignon,  E.; Varni,  G.;  and <a class=\"bibbase author link\" href=\"https://maoiproject.github.io/publications/\">Clavel,  C.</a>\n</span>\n\n  \n\n</span>\n\n  <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, 34(05). 4 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1609/aaai.v34i05.6259\"\n     onclick=\"log_download('colombo-chapuis-manica-vignon-varni-clavel-guidingattentioninsequencetosequencemodelsfordialogueactprediction-2020', 'http://doi.org/10.1609/aaai.v34i05.6259', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/colombo-chapuis-manica-vignon-varni-clavel-guidingattentioninsequencetosequencemodelsfordialogueactprediction-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('colombo-chapuis-manica-vignon-varni-clavel-guidingattentioninsequencetosequencemodelsfordialogueactprediction-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Guiding Attention in Sequence-to-Sequence Models for Dialogue Act Prediction},\n type = {article},\n year = {2020},\n volume = {34},\n month = {4},\n day = {3},\n id = {157703f0-fe46-3486-afab-9f9f3ac97356},\n created = {2021-04-20T12:48:05.251Z},\n file_attached = {false},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},\n last_modified = {2021-04-20T12:48:05.251Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {&lt;p&gt;The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translation (NMT) to improve the modelling of tag sequentiality. Seq2seq models are known to learn complex global dependencies while currently proposed approaches using linear conditional random fields (CRF) only model local tag dependencies. In this work, we introduce a seq2seq model tailored for DA classification using: a hierarchical encoder, a novel guided attention mechanism and beam search applied to both training and inference. Compared to the state of the art our model does not require handcrafted features and is trained end-to-end. Furthermore, the proposed approach achieves an unmatched accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on MRDA.&lt;/p&gt;},\n bibtype = {article},\n author = {Colombo, Pierre and Chapuis, Emile and Manica, Matteo and Vignon, Emmanuel and Varni, Giovanna and Clavel, Chloe},\n doi = {10.1609/aaai.v34i05.6259},\n journal = {Proceedings of the AAAI Conference on Artificial Intelligence},\n number = {05}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  <p>The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translation (NMT) to improve the modelling of tag sequentiality. Seq2seq models are known to learn complex global dependencies while currently proposed approaches using linear conditional random fields (CRF) only model local tag dependencies. In this work, we introduce a seq2seq model tailored for DA classification using: a hierarchical encoder, a novel guided attention mechanism and beam search applied to both training and inference. Compared to the state of the art our model does not require handcrafted features and is trained end-to-end. Furthermore, the proposed approach achieves an unmatched accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on MRDA.</p>\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chapuis-colombo-manica-labeau-clavel-hierarchicalpretrainingforsequencelabellinginspokendialog-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/09f0fed5-76a2-83f3-7121-10654c99f1b5/full_text.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chapuis-colombo-manica-labeau-clavel-hierarchicalpretrainingforsequencelabellinginspokendialog-2020', 'https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/09f0fed5-76a2-83f3-7121-10654c99f1b5/full_text.pdf.pdf', event);\">\n    Hierarchical Pre-training for Sequence Labelling in Spoken Dialog.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chapuis,  E.; Colombo,  P.; Manica,  M.; Labeau,  M.;  and <a class=\"bibbase author link\" href=\"https://maoiproject.github.io/publications/\">Clavel,  C.</a>\n</span>\n\n  \n\n</span>\n\n  . 9 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/09f0fed5-76a2-83f3-7121-10654c99f1b5/full_text.pdf.pdf\"\n     onclick=\"log_download('chapuis-colombo-manica-labeau-clavel-hierarchicalpretrainingforsequencelabellinginspokendialog-2020', 'https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/09f0fed5-76a2-83f3-7121-10654c99f1b5/full_text.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Hierarchical Pre-training for Sequence Labelling in Spoken Dialog [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://arxiv.org/abs/2009.11152\"\n     onclick=\"log_download('chapuis-colombo-manica-labeau-clavel-hierarchicalpretrainingforsequencelabellinginspokendialog-2020', 'http://arxiv.org/abs/2009.11152', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Hierarchical Pre-training for Sequence Labelling in Spoken Dialog [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chapuis-colombo-manica-labeau-clavel-hierarchicalpretrainingforsequencelabellinginspokendialog-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chapuis-colombo-manica-labeau-clavel-hierarchicalpretrainingforsequencelabellinginspokendialog-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Hierarchical Pre-training for Sequence Labelling in Spoken Dialog},\n type = {article},\n year = {2020},\n websites = {http://arxiv.org/abs/2009.11152},\n month = {9},\n day = {23},\n id = {8beb5535-2ca0-3d30-a16d-10cd26b5f67e},\n created = {2021-04-20T12:48:05.799Z},\n accessed = {2021-04-20},\n file_attached = {true},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},\n last_modified = {2021-04-20T12:48:07.281Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (\\textttSILICONE). \\textttSILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over $2.3$ billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.},\n bibtype = {article},\n author = {Chapuis, Emile and Colombo, Pierre and Manica, Matteo and Labeau, Matthieu and Clavel, Chloe}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (\\textttSILICONE). \\textttSILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over $2.3$ billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"colombo-chapuis-manica-vignon-varni-clavel-guiderlattentiondanslesmodelesdesequenceasequencepourlapredictiondesactesdedialogue-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/57805f2b-8013-89ef-7b05-4dc9ee8b7a49/full_text.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'colombo-chapuis-manica-vignon-varni-clavel-guiderlattentiondanslesmodelesdesequenceasequencepourlapredictiondesactesdedialogue-2020', 'https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/57805f2b-8013-89ef-7b05-4dc9ee8b7a49/full_text.pdf.pdf', event);\">\n    Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Colombo,  P.; Chapuis,  E.; Manica,  M.; Vignon,  E.; Varni,  G.;  and <a class=\"bibbase author link\" href=\"https://maoiproject.github.io/publications/\">Clavel,  C.</a>\n</span>\n\n  \n\n</span>\n\n  . 2 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/57805f2b-8013-89ef-7b05-4dc9ee8b7a49/full_text.pdf.pdf\"\n     onclick=\"log_download('colombo-chapuis-manica-vignon-varni-clavel-guiderlattentiondanslesmodelesdesequenceasequencepourlapredictiondesactesdedialogue-2020', 'https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/57805f2b-8013-89ef-7b05-4dc9ee8b7a49/full_text.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Guider l&#x27;attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://arxiv.org/abs/2002.09419\"\n     onclick=\"log_download('colombo-chapuis-manica-vignon-varni-clavel-guiderlattentiondanslesmodelesdesequenceasequencepourlapredictiondesactesdedialogue-2020', 'https://arxiv.org/abs/2002.09419', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Guider l&#x27;attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/colombo-chapuis-manica-vignon-varni-clavel-guiderlattentiondanslesmodelesdesequenceasequencepourlapredictiondesactesdedialogue-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('colombo-chapuis-manica-vignon-varni-clavel-guiderlattentiondanslesmodelesdesequenceasequencepourlapredictiondesactesdedialogue-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Guider l&#x27;attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue},\n type = {article},\n year = {2020},\n websites = {https://arxiv.org/abs/2002.09419},\n month = {2},\n day = {21},\n id = {eb1e9b02-d394-3697-b2ed-33a1e0d81d80},\n created = {2021-04-20T12:48:52.944Z},\n accessed = {2021-04-20},\n file_attached = {true},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},\n last_modified = {2021-04-20T12:48:53.467Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {The task of predicting dialog acts (DA) based on conversational dialog is a\nkey component in the development of conversational agents. Accurately\npredicting DAs requires a precise modeling of both the conversation and the\nglobal tag dependencies. We leverage seq2seq approaches widely adopted in\nNeural Machine Translation (NMT) to improve the modelling of tag sequentiality.\nSeq2seq models are known to learn complex global dependencies while currently\nproposed approaches using linear conditional random fields (CRF) only model\nlocal tag dependencies. In this work, we introduce a seq2seq model tailored for\nDA classification using: a hierarchical encoder, a novel guided attention\nmechanism and beam search applied to both training and inference. Compared to\nthe state of the art our model does not require handcrafted features and is\ntrained end-to-end. Furthermore, the proposed approach achieves an unmatched\naccuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on\nMRDA.},\n bibtype = {article},\n author = {Colombo, Pierre and Chapuis, Emile and Manica, Matteo and Vignon, Emmanuel and Varni, Giovanna and Clavel, Chloe}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The task of predicting dialog acts (DA) based on conversational dialog is a\nkey component in the development of conversational agents. Accurately\npredicting DAs requires a precise modeling of both the conversation and the\nglobal tag dependencies. We leverage seq2seq approaches widely adopted in\nNeural Machine Translation (NMT) to improve the modelling of tag sequentiality.\nSeq2seq models are known to learn complex global dependencies while currently\nproposed approaches using linear conditional random fields (CRF) only model\nlocal tag dependencies. In this work, we introduce a seq2seq model tailored for\nDA classification using: a hierarchical encoder, a novel guided attention\nmechanism and beam search applied to both training and inference. Compared to\nthe state of the art our model does not require handcrafted features and is\ntrained end-to-end. Furthermore, the proposed approach achieves an unmatched\naccuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on\nMRDA.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"atamna-clavel-hrirnnauserrobotdynamicsorientedrnnforengagementdecreasedetection-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Atamna,  A.;  and <a class=\"bibbase author link\" href=\"https://maoiproject.github.io/publications/\">Clavel,  C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>INTERSPEECH 2020</i>,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/atamna-clavel-hrirnnauserrobotdynamicsorientedrnnforengagementdecreasedetection-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('atamna-clavel-hrirnnauserrobotdynamicsorientedrnnforengagementdecreasedetection-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection},\n type = {inproceedings},\n year = {2020},\n id = {3850cc8d-371e-3313-9d8d-1c6c0a58d228},\n created = {2021-04-22T16:44:17.606Z},\n file_attached = {false},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},\n last_modified = {2021-04-22T16:44:17.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Atamna, Asma and Clavel, Chloé},\n booktitle = {INTERSPEECH 2020}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"barriere-clavel-essid-opiniondynamicsmodelingformoviereviewtranscriptsclassificationwithhiddenconditionalrandomfields-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/0d571117-d2b0-ac47-63c8-a644a7d9e3a5/full_text.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'barriere-clavel-essid-opiniondynamicsmodelingformoviereviewtranscriptsclassificationwithhiddenconditionalrandomfields-2018', 'https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/0d571117-d2b0-ac47-63c8-a644a7d9e3a5/full_text.pdf.pdf', event);\">\n    Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Barriere,  V.; <a class=\"bibbase author link\" href=\"https://maoiproject.github.io/publications/\">Clavel,  C.</a>;  and Essid,  S.\n</span>\n\n  \n\n</span>\n\n  . 6 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/0d571117-d2b0-ac47-63c8-a644a7d9e3a5/full_text.pdf.pdf\"\n     onclick=\"log_download('barriere-clavel-essid-opiniondynamicsmodelingformoviereviewtranscriptsclassificationwithhiddenconditionalrandomfields-2018', 'https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/0d571117-d2b0-ac47-63c8-a644a7d9e3a5/full_text.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://arxiv.org/abs/1806.07787\"\n     onclick=\"log_download('barriere-clavel-essid-opiniondynamicsmodelingformoviereviewtranscriptsclassificationwithhiddenconditionalrandomfields-2018', 'http://arxiv.org/abs/1806.07787', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/barriere-clavel-essid-opiniondynamicsmodelingformoviereviewtranscriptsclassificationwithhiddenconditionalrandomfields-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('barriere-clavel-essid-opiniondynamicsmodelingformoviereviewtranscriptsclassificationwithhiddenconditionalrandomfields-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields},\n type = {article},\n year = {2018},\n websites = {http://arxiv.org/abs/1806.07787},\n month = {6},\n day = {20},\n id = {26e73a13-c65f-31fc-9a3d-25edbdb49420},\n created = {2021-04-20T12:48:04.692Z},\n accessed = {2021-04-20},\n file_attached = {true},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},\n last_modified = {2021-04-20T12:48:06.831Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {In this paper, the main goal is to detect a movie reviewer&#x27;s opinion using hidden conditional random fields. This model allows us to capture the dynamics of the reviewer&#x27;s opinion in the transcripts of long unsegmented audio reviews that are analyzed by our system. High level linguistic features are computed at the level of inter-pausal segments. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the ICT-MMMO corpus. We obtain a F1-score of 82\\%, which is better than logistic regression and recurrent neural network approaches. We also offer a discussion that sheds some light on the capacity of our system to adapt the word embedding model learned from general written texts data to spoken movie reviews and thus model the dynamics of the opinion.},\n bibtype = {article},\n author = {Barriere, Valentin and Clavel, Chloé and Essid, Slim}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In this paper, the main goal is to detect a movie reviewer's opinion using hidden conditional random fields. This model allows us to capture the dynamics of the reviewer's opinion in the transcripts of long unsegmented audio reviews that are analyzed by our system. High level linguistic features are computed at the level of inter-pausal segments. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the ICT-MMMO corpus. We obtain a F1-score of 82\\%, which is better than logistic regression and recurrent neural network approaches. We also offer a discussion that sheds some light on the capacity of our system to adapt the word embedding model learned from general written texts data to spoken movie reviews and thus model the dynamics of the opinion.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2015\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2015')\"\n      onkeypress=\"toggleGroup('group_2015')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2015</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"langlet-clavel-adaptingsentimentanalysistofacetofacehumanagentinteractionsfromthedetectiontotheevaluationissues-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/111b973f-7cba-b2ff-203e-891ad8d9f665/07344545.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'langlet-clavel-adaptingsentimentanalysistofacetofacehumanagentinteractionsfromthedetectiontotheevaluationissues-2015', 'https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/111b973f-7cba-b2ff-203e-891ad8d9f665/07344545.pdf.pdf', event);\">\n    Adapting sentiment analysis to face-to-face human-agent interactions: From the detection to the evaluation issues.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Langlet,  C.;  and <a class=\"bibbase author link\" href=\"https://maoiproject.github.io/publications/\">Clavel,  C.</a>\n</span>\n\n  \n\n</span>\n\n  <i>2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015</i>,14-20.  2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/111b973f-7cba-b2ff-203e-891ad8d9f665/07344545.pdf.pdf\"\n     onclick=\"log_download('langlet-clavel-adaptingsentimentanalysistofacetofacehumanagentinteractionsfromthedetectiontotheevaluationissues-2015', 'https://bibbase.org/service/mendeley/1bff199d-3fb6-39f8-9a95-8103f3a5d433/file/111b973f-7cba-b2ff-203e-891ad8d9f665/07344545.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Adapting sentiment analysis to face-to-face human-agent interactions: From the detection to the evaluation issues [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ACII.2015.7344545\"\n     onclick=\"log_download('langlet-clavel-adaptingsentimentanalysistofacetofacehumanagentinteractionsfromthedetectiontotheevaluationissues-2015', 'http://doi.org/10.1109/ACII.2015.7344545', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/langlet-clavel-adaptingsentimentanalysistofacetofacehumanagentinteractionsfromthedetectiontotheevaluationissues-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('langlet-clavel-adaptingsentimentanalysistofacetofacehumanagentinteractionsfromthedetectiontotheevaluationissues-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Adapting sentiment analysis to face-to-face human-agent interactions: From the detection to the evaluation issues},\n type = {article},\n year = {2015},\n keywords = {alignment,embodied conversational agent,emotional stance,engagement,other-repetition},\n pages = {14-20},\n publisher = {IEEE},\n id = {92200a35-b123-3234-a284-c41d8d0da577},\n created = {2021-04-20T12:48:06.227Z},\n file_attached = {true},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {1619b2da-6d2b-31f6-b805-355bf28ff212},\n last_modified = {2021-04-20T12:48:07.714Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2015 IEEE. This paper introduces a sentiment analysis method suitable to the human-agent and face-to-face interactions. We present the positioning of our system and its evaluation protocol according to the existing sentiment analysis literature and detail how the proposed system integrates the human-agent interaction issues. Finally, we provide an in-depth analysis of the results obtained by the evaluation, opening the discussion on the different difficulties and the remaining challenges of sentiment analysis in human-agent interactions.},\n bibtype = {article},\n author = {Langlet, Caroline and Clavel, Chloe},\n doi = {10.1109/ACII.2015.7344545},\n journal = {2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  © 2015 IEEE. This paper introduces a sentiment analysis method suitable to the human-agent and face-to-face interactions. We present the positioning of our system and its evaluation protocol according to the existing sentiment analysis literature and detail how the proposed system integrates the human-agent interaction issues. Finally, we provide an in-depth analysis of the results obtained by the evaluation, opening the discussion on the different difficulties and the remaining challenges of sentiment analysis in human-agent interactions.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);