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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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Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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Modelling Latent Translations for Cross-Lingual Transfer ...
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Prix-LM: Pretraining for Multilingual Knowledge Base Construction ...
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Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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Abstract:
Capturing word meaning in context and distinguishing between correspondences and variations across languages is key to building successful multilingual and cross-lingual text representation models. However, existing multilingual evaluation datasets that evaluate lexical semantics "in-context" have various limitations. In particular, 1) their language coverage is restricted to high-resource languages and skewed in favor of only a few language families and areas, 2) a design that makes the task solvable via superficial cues, which results in artificially inflated (and sometimes super-human) performances of pretrained encoders, on many target languages, which limits their usefulness for model probing and diagnostics, and 3) little support for cross-lingual evaluation. In order to address these gaps, we present AM2iCo (Adversarial and Multilingual Meaning in Context), a wide-coverage cross-lingual and multilingual evaluation set; it aims to faithfully assess the ability of state-of-the-art (SotA) representation ... : EMNLP 2021 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/2104.08639 https://dx.doi.org/10.48550/arxiv.2104.08639
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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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Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer ...
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MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ...
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How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
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