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From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers
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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 resource-lean languages where only a small corpus or a bilingual dictionary is available, and report substantial improvements over the random baseline. The XCOPA dataset is freely available at github.com/cambridgeltl/xcopa
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URL: https://doi.org/10.17863/CAM.62209 https://www.repository.cam.ac.uk/handle/1810/315102
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XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages
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Glavas, Goran; Karan, Mladen; Vulic, Ivan. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.559, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020
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Specializing unsupervised pretraining models for word-level semantic similarity
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Non-linear instance-based cross-lingual mapping for non-isomorphic embedding spaces
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Classification-based self-learning for weakly supervised bilingual lexicon induction
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AraWEAT: Multidimensional analysis of biases in Arabic word embeddings
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Common sense or world knowledge? Investigating adapter-based knowledge injection into pretrained transformers
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XHate-999: analyzing and detecting abusive language across domains and languages
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On the limitations of cross-lingual encoders as exposed by reference-free machine translation evaluation
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XCOPA: A multilingual dataset for causal commonsense reasoning
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Improving bilingual lexicon induction with unsupervised post-processing of monolingual word vector spaces
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From zero to hero: On the limitations of zero-shot language transfer with multilingual transformers
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SemEval-2020 Task 2: Predicting multilingual and cross-lingual (graded) lexical entailment
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Towards instance-level parser selection for cross-lingual transfer of dependency parsers
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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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How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions ...
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Specialising Distributional Vectors of All Words for Lexical Entailment ...
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