Papers with WE
A Rank-Based Similarity Metric for Word Embeddings (P18-2)
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| Challenge: | Word Embeddings have become a standard for word representations, with vector cosine being the only similarity metric. |
| Approach: | They propose to use rank-based similarity estimation metrics to measure word similarity . they find WE outperforms vector cosine in the recent outlier detection task . |
| Outcome: | The proposed rank-based measure outperforms vector cosine in the recent outlier detection task. |
LexFit: Lexical Fine-Tuning of Pretrained Language Models (2021.acl-long)
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| Challenge: | Transformer-based language models implicitly store a wealth of lexical semantic knowledge, but it is non-trivial to extract that knowledge effectively from their parameters. |
| Approach: | They propose to expose and enrich lexical knowledge from transformer-based language models to serve as effective decontextualized word encoders even when fed input words "in isolation" |
| Outcome: | The proposed model outperforms standard static WEs and vanilla LMs in lexical tasks over four established tasks in 8 languages. |
Evaluating Word Expansion for Multilingual Sentiment Analysis of Parliamentary Speech (2024.lrec-main)
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| Challenge: | Recent efforts to create and format data sets of parliamentary speech material have facilitated cross-lingual comparisons and highlighted the need for methods that are computationally efficient and language-agnostic. |
| Approach: | They propose a word expansion method for sentiment lexicon generation that leverages word embeddings and vector similarity to expand synonym seed lists with domain-specific terms from the speech corpora. |
| Outcome: | The proposed method is compared with other multilingual lexica and is highly sensitive to processing and scoring techniques. |
Word Embedding Evaluation in Downstream Tasks and Semantic Analogies (2020.lrec-1)
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| Challenge: | Language Models (LMs) are an oft studied area of natural language processing . Word Embeddings (WE) are vector space representations of a vocabulary . |
| Approach: | They evaluate Word Embeddings (WE) models for the Portuguese langauage . results show that a diverse corpus can often outperform a larger, less textually diverse corp. |
| Outcome: | The proposed models outperform a larger, less textually diverse corpus in two tasks . the evaluation shows that a diverse and comprehensive corpus outperformed a smaller, less diverse corp. |
Unpacking Bias: An Empirical Study of Bias Measurement Metrics, Mitigation Algorithms, and Their Interactions (2024.lrec-main)
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| Challenge: | Word embeddings (WE) models reflect gender, racial, and religious stereotypes from the corpus on which they are trained. |
| Approach: | They propose a method that carefully controls for word sets and vector normalization to address these factors. |
| Outcome: | The proposed method detects consistency between different mitigation methods and the evaluation words used by the mitigation methods. |