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RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
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LexFit: Lexical Fine-Tuning of Pretrained Language Models ...
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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Abstract:
Unsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications. These models, however, retain some of the limitations of traditional static word embeddings. In particular, they encode only the distributional knowledge available in raw text corpora, incorporated through language modeling objectives. In this work, we complement such distributional knowledge with external lexical knowledge, that is, we integrate the discrete knowledge on word-level semantic similarity into pretraining. To this end, we generalize the standard BERT model to a multi-task learning setting where we couple BERT’s masked language modeling and next sentence prediction objectives with an auxiliary task of binary word relation classification. Our experiments suggest that our “Lexically Informed” BERT (LIBERT), specialized for the word-level semantic similarity, yields better performance than the lexically blind “vanilla” BERT on several language understanding tasks. Concretely, LIBERT ...
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Keyword:
Computer and Information Science; Natural Language Processing; Neural Network
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URL: https://dx.doi.org/10.48448/j696-8h54
https://underline.io/lecture/6311-specializing-unsupervised-pretraining-models-for-word-level-semantic-similarity
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