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Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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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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RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
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LexFit: Lexical Fine-Tuning of Pretrained Language Models ...
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Verb Knowledge Injection for Multilingual Event Processing ...
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Is supervised syntactic parsing beneficial for language understanding tasks? An empirical investigation
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Evaluating multilingual text encoders for unsupervised cross-lingual retrieval
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Training and domain adaptation for supervised text segmentation
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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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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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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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URL: https://www.repository.cam.ac.uk/handle/1810/315102 https://dx.doi.org/10.17863/cam.62209
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From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers ...
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Verb Knowledge Injection for Multilingual Event Processing ...
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