mlsanityData to 14 Oct 2026

Using Paraphrases to Study Properties of Contextual Embeddings

arXiv 2207.05553 ↗First posted 12 Jul 2022cs.CLcited by 3

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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.

Builds on

14 of 33 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  4. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  5. 2020A Primer in BERTology: What we know about how BERT workscited by 815
  6. 2019How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddingscited by 784
  7. 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 396
  8. 2019Revealing the Dark Secrets of BERTcited by 301
  9. 2013Exploiting Similarities among Languages for Machine Translationcited by 191
  10. 2019GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledgecited by 79
  11. 2019Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddingscited by 56
  12. 2020Assessing Phrasal Representation and Composition in Transformerscited by 41
2 more
  1. 2016Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learningcited by 22
  2. 2020BERT Knows Punta Cana is not just beautiful, it's gorgeous: Ranking Scalar Adjectives with Contextualised Representationscited by 15

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Citing papers per year: 2023: 2; 2024: 1.

3 papers.