Papers with Contextualized word embeddings
Retrofitting Contextualized Word Embeddings with Paraphrases (D19-1)
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| Challenge: | Contextualized word embeddings can be useful for downstream applications, but they can be over-sensitive to contexts. |
| Approach: | They propose a method to retrofit contextualized word embeddings with paraphrases to minimize the variance of word representations on paraphrased contexts. |
| Outcome: | The proposed method improves on sentence classification and inference tasks. |
With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation (2020.emnlp-main)
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| Challenge: | Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, but it is difficult to link them to structured sources of knowledge. |
| Approach: | They propose a semi-supervised approach to producing sense embeddings for the lexical meanings within a lexicon that is comparable to that of contextualized word vectors. |
| Outcome: | The proposed approach outperforms state-of-the-art models in the English Word Sense Disambiguation task and in the multilingual one while training on sense-annotated data in English only. |
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling (2024.lrec-main)
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| Challenge: | Existing topic models rely on bag-of-words (BOW) representations to capture word order information. |
| Approach: | They propose a neural topic model that integrates contextualized word embeddings from BERT to learn the topic vector of a document without BOW information. |
| Outcome: | The proposed model generates more coherent and meaningful topics compared to existing models while accommodating unseen words in newly encountered documents. |
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling (D19-1)
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| Challenge: | Contextualized word embeddings are becoming a ubiquitous component of natural language processing. |
| Approach: | They propose a domain-adaptive fine-tuning approach to pretrain on unlabeled text . they test this approach on sequence labeling in two challenging domains . |
| Outcome: | The proposed approach improves on sequence labeling in two domains: Early Modern English and Twitter. |
What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition (D19-1)
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| Challenge: | Contextualized word embeddings have boosted many NLP tasks compared with static word embeds. |
| Approach: | They propose a framework that can explain word meanings given contextualized word embeddings for better interpretation. |
| Outcome: | The proposed framework can explain word meanings given contextualized word embeddings for better interpretation. |