Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration

• URL: http://arxiv.org/abs/2109.06304v1
• Date: Mon, 13 Sep 2021 20:31:57 GMT
• Title: Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration
• Authors: Shufan Wang and Laure Thompson and Mohit Iyyer
• Abstract summary: We propose a contrastive fine-tuning objective that enables BERT to produce more powerful phrase embeddings. Our approach relies on a dataset of diverse phrasal paraphrases, which is automatically generated using a paraphrase generation model. As a case study, we show that Phrase-BERT embeddings can be easily integrated with a simple autoencoder to build a phrase-based neural topic model.
• Score: 25.159601117722936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Phrase representations derived from BERT often do not exhibit complex phrasal compositionality, as the model relies instead on lexical similarity to determine semantic relatedness. In this paper, we propose a contrastive fine-tuning objective that enables BERT to produce more powerful phrase embeddings. Our approach (Phrase-BERT) relies on a dataset of diverse phrasal paraphrases, which is automatically generated using a paraphrase generation model, as well as a large-scale dataset of phrases in context mined from the Books3 corpus. Phrase-BERT outperforms baselines across a variety of phrase-level similarity tasks, while also demonstrating increased lexical diversity between nearest neighbors in the vector space. Finally, as a case study, we show that Phrase-BERT embeddings can be easily integrated with a simple autoencoder to build a phrase-based neural topic model that interprets topics as mixtures of words and phrases by performing a nearest neighbor search in the embedding space. Crowdsourced evaluations demonstrate that this phrase-based topic model produces more coherent and meaningful topics than baseline word and phrase-level topic models, further validating the utility of Phrase-BERT.

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