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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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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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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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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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