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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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Abstract:
Performance in cross-lingual NLP tasks is impacted by the (dis)similarity of languages at hand: e.g., previous work has suggested there is a connection between the expected success of bilingual lexicon induction (BLI) and the assumption of (approximate) isomorphism between monolingual embedding spaces. In this work we present a large-scale study focused on the correlations between monolingual embedding space similarity and task performance, covering thousands of language pairs and four different tasks: BLI, parsing, POS tagging and MT. We hypothesize that statistics of the spectrum of each monolingual embedding space indicate how well they can be aligned. We then introduce several isomorphism measures between two embedding spaces, based on the relevant statistics of their individual spectra. We empirically show that 1) language similarity scores derived from such spectral isomorphism measures are strongly associated with performance observed in different cross-lingual tasks, and 2) our spectral-based ... : 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/2001.11136 https://dx.doi.org/10.48550/arxiv.2001.11136
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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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