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EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification ...
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Delving Deeper into Cross-lingual Visual Question Answering ...
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Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ...
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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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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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Abstract:
In order to simulate human language capacity, natural language processing systems must be able to reason about the dynamics of everyday situations, including their possible causes and effects. Moreover, they should be able to generalise the acquired world knowledge to new languages, modulo cultural differences. Advances in machine reasoning and cross-lingual transfer depend on the availability of challenging evaluation benchmarks. Motivated by both demands, we introduce Cross-lingual Choice of Plausible Alternatives (XCOPA), a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, which includes resource-poor languages like Eastern Apurímac Quechua and Haitian Creole. We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods based on multilingual pretraining and zero-shot fine-tuning falls short compared to translation-based transfer. Finally, we propose strategies to adapt multilingual models to out-of-sample ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2005.00333 https://dx.doi.org/10.48550/arxiv.2005.00333
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Emergent Communication Pretraining for Few-Shot Machine Translation ...
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