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Data for paper: "Evaluating Resource-Lean Cross-Lingual Embedding Models in Unsupervised Retrieval" ...
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Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings ...
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XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation ...
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Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer ...
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From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers ...
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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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Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ...
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Informing unsupervised pretraining with external linguistic knowledge
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Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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A Resource-Light Method for Cross-Lingual Semantic Textual Similarity ...
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