Sentence Simplification with Memory-Augmented Neural Networks. Vu, T., Hu, B., Munkhdalai, T., & Yu, H. In North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018. doi abstract bibtex Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications. Recent advances in neural machine translation have paved the way for novel approaches to the task. In this paper, we adapt an architecture with augmented memory capacities called Neural Semantic Encoders (Munkhdalai and Yu, 2017) for sentence simplification. Our experiments demonstrate the effectiveness of our approach on different simplification datasets, both in terms of automatic evaluation measures and human judgments.
@inproceedings{vu_sentence_2018,
title = {Sentence {Simplification} with {Memory}-{Augmented} {Neural} {Networks}},
doi = {DOI:10.18653/v1/N18-2013},
abstract = {Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications. Recent advances in neural machine translation have paved the way for novel approaches to the task. In this paper, we adapt an architecture with augmented memory capacities called Neural Semantic Encoders (Munkhdalai and Yu, 2017) for sentence simplification. Our experiments demonstrate the effectiveness of our approach on different simplification datasets, both in terms of automatic evaluation measures and human judgments.},
booktitle = {North {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies}},
author = {Vu, Tu and Hu, Baotian and Munkhdalai, Tsendsuren and Yu, Hong},
year = {2018},
}
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