Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext

John Wieting, Jonathan Mallinson, Kevin Gimpel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of Wieting et al. (2016b). We use neural machine translation to generate sentential paraphrases via back- translation of bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to serve as training data for learning paraphrastic sentence embeddings. We find that the data quality is stronger than prior work based on bitext and on par with manually-written English paraphrase pairs, with the advantage that our approach can scale up to generate large training sets for many languages and domains. We experiment with several language pairs and data sources, and develop a variety of data filtering techniques. In the process, we explore how neural machine translation output differs from human-written sentences, finding clear differences in length, the amount of repetition, and the use of rare words.1

1: Generated paraphrases and code are available at http://ttic.uchicago.edu/˜wieting.
Original languageEnglish
Title of host publicationProceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics
Pages274-285
Number of pages12
DOIs
Publication statusPublished - 11 Sep 2017
EventEMNLP 2017: Conference on Empirical Methods in Natural Language Processing - Copenhagen, Denmark
Duration: 7 Sep 201711 Sep 2017
http://emnlp2017.net/index.html
http://emnlp2017.net/

Conference

ConferenceEMNLP 2017: Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2017
CountryDenmark
CityCopenhagen
Period7/09/1711/09/17
Internet address

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