Using Paraphrases to Study Properties of Contextual Embeddings
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Abstract (arXiv)
We use paraphrases as a unique source of data to analyze contextualized embeddings, with a particular focus on BERT. Because paraphrases naturally encode consistent word and phrase semantics, they provide a unique lens for investigating properties of embeddings. Using the Paraphrase Database's alignments, we study words within paraphrases as well as phrase representations. We find that contextual embeddings effectively handle polysemous words, but give synonyms surprisingly different representations in many cases. We confirm previous findings that BERT is sensitive to word order, but find slightly different patterns than prior work in terms of the level of contextualization across BERT's layers.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 12 Jul 2022.
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14 of 33 references.
References · 14
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- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
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- 2019How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddingscited by 784
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- 2013Exploiting Similarities among Languages for Machine Translationcited by 191
- 2019GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledgecited by 79
- 2019Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddingscited by 56
- 2020Assessing Phrasal Representation and Composition in Transformerscited by 41
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Citing papers per year: 2023: 2; 2024: 1.
3 papers.