@inproceedings{zhou-etal-2017-learning,
title = "Learning Phrase Embeddings from Paraphrases with {GRU}s",
author = "Zhou, Zhihao and
Huang, Lifu and
Ji, Heng",
editor = "Afli, Haithem and
Liu, Chao-Hong",
booktitle = "Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/W17-5603",
pages = "16--23",
abstract = "Learning phrase representations has been widely explored in many Natural Language Processing tasks (e.g., Sentiment Analysis, Machine Translation) and has shown promising improvements. Previous studies either learn non-compositional phrase representations with general word embedding learning techniques or learn compositional phrase representations based on syntactic structures, which either require huge amounts of human annotations or cannot be easily generalized to all phrases. In this work, we propose to take advantage of large-scaled paraphrase database and present a pairwise-GRU framework to generate compositional phrase representations. Our framework can be re-used to generate representations for any phrases. Experimental results show that our framework achieves state-of-the-art results on several phrase similarity tasks.",
}
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%0 Conference Proceedings
%T Learning Phrase Embeddings from Paraphrases with GRUs
%A Zhou, Zhihao
%A Huang, Lifu
%A Ji, Heng
%Y Afli, Haithem
%Y Liu, Chao-Hong
%S Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F zhou-etal-2017-learning
%X Learning phrase representations has been widely explored in many Natural Language Processing tasks (e.g., Sentiment Analysis, Machine Translation) and has shown promising improvements. Previous studies either learn non-compositional phrase representations with general word embedding learning techniques or learn compositional phrase representations based on syntactic structures, which either require huge amounts of human annotations or cannot be easily generalized to all phrases. In this work, we propose to take advantage of large-scaled paraphrase database and present a pairwise-GRU framework to generate compositional phrase representations. Our framework can be re-used to generate representations for any phrases. Experimental results show that our framework achieves state-of-the-art results on several phrase similarity tasks.
%U https://aclanthology.org/W17-5603
%P 16-23
Markdown (Informal)
[Learning Phrase Embeddings from Paraphrases with GRUs](https://aclanthology.org/W17-5603) (Zhou et al., 2017)
ACL
- Zhihao Zhou, Lifu Huang, and Heng Ji. 2017. Learning Phrase Embeddings from Paraphrases with GRUs. In Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora, pages 16–23, Taipei, Taiwan. Asian Federation of Natural Language Processing.