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Delving Deeper into Cross-lingual Visual Question Answering ...
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Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation ...
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Improving Word Translation via Two-Stage Contrastive Learning ...
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Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification ...
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MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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Context vs Target Word: Quantifying Biases in Lexical Semantic Datasets ...
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AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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Emergent Communication Pretraining for Few-Shot Machine Translation ...
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A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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Verb Knowledge Injection for Multilingual Event Processing ...
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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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Probing Pretrained Language Models for Lexical Semantics ...
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The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures ...
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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:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1909.02339 https://dx.doi.org/10.48550/arxiv.1909.02339
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Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ...
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