Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue. Balakrishnan, A., Rao, J., Upasani, K., White, M., & Subba, R. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 831–844, Florence, Italy, July, 2019. Association for Computational Linguistics.
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue [link]Paper  abstract   bibtex   1 download  
Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems. Avenues like the E2E NLG Challenge have encouraged the development of neural approaches, particularly sequence-to-sequence (Seq2Seq) models for this problem. The semantic representations used, however, are often underspecified, which places a higher burden on the generation model for sentence planning, and also limits the extent to which generated responses can be controlled in a live system. In this paper, we (1) propose using tree-structured semantic representations, like those used in traditional rule-based NLG systems, for better discourse-level structuring and sentence-level planning; (2) introduce a challenging dataset using this representation for the weather domain; (3) introduce a constrained decoding approach for Seq2Seq models that leverages this representation to improve semantic correctness; and (4) demonstrate promising results on our dataset and the E2E dataset.
@inproceedings{balakrishnan-etal-2019-constrained,
    title = "Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue",
    author = "Balakrishnan, Anusha  and
      Rao, Jinfeng  and
      Upasani, Kartikeya  and
      White, Michael  and
      Subba, Rajen",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1080",
    pages = "831--844",
    abstract = "Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems. Avenues like the E2E NLG Challenge have encouraged the development of neural approaches, particularly sequence-to-sequence (Seq2Seq) models for this problem. The semantic representations used, however, are often underspecified, which places a higher burden on the generation model for sentence planning, and also limits the extent to which generated responses can be controlled in a live system. In this paper, we (1) propose using tree-structured semantic representations, like those used in traditional rule-based NLG systems, for better discourse-level structuring and sentence-level planning; (2) introduce a challenging dataset using this representation for the weather domain; (3) introduce a constrained decoding approach for Seq2Seq models that leverages this representation to improve semantic correctness; and (4) demonstrate promising results on our dataset and the E2E dataset.",
}

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