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XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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Probing Pretrained Language Models for Lexical Semantics ...
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Abstract:
The success of large pretrained language models (LMs) such as BERT and RoBERTa has sparked interest in probing their representations, in order to unveil what types of knowledge they implicitly capture. While prior research focused on morphosyntactic, semantic, and world knowledge, it remains unclear to which extent LMs also derive lexical type-level knowledge from words in context. In this work, we present a systematic empirical analysis across six typologically diverse languages and five different lexical tasks, addressing the following questions: 1) How do different lexical knowledge extraction strategies (monolingual versus multilingual source LM, out-of-context versus in-context encoding, inclusion of special tokens, and layer-wise averaging) impact performance? How consistent are the observed effects across tasks and languages? 2) Is lexical knowledge stored in few parameters, or is it scattered throughout the network? 3) How do these representations fare against traditional static word vectors in ... : EMNLP 2020: Long paper ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2010.05731 https://dx.doi.org/10.48550/arxiv.2010.05731
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Specializing unsupervised pretraining models for word-level semantic similarity
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XCOPA: A multilingual dataset for causal commonsense reasoning
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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Specializing distributional vectors of all words for lexical entailment
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Cross-lingual semantic specialization via lexical relation induction
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Informing unsupervised pretraining with external linguistic knowledge
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Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization
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