Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Using Paraphrases to Study Properties of Contextual Embeddings (2207.05553v1)

Published 12 Jul 2022 in cs.CL

Abstract: 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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Laura Burdick (3 papers)
  2. Jonathan K. Kummerfeld (38 papers)
  3. Rada Mihalcea (131 papers)
Citations (4)

Summary

We haven't generated a summary for this paper yet.