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1
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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2
LexFit: Lexical Fine-Tuning of Pretrained Language Models ...
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3
Verb Knowledge Injection for Multilingual Event Processing ...
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4
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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5
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
Ponti, Edoardo; Glavaš, Goran; Majewska, Olga. - : Apollo - University of Cambridge Repository, 2020
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6
Verb Knowledge Injection for Multilingual Event Processing ...
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7
Probing Pretrained Language Models for Lexical Semantics ...
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8
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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9
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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10
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
Liu, Qianchu; Korhonen, Anna-Leena; Majewska, Olga; Ponti, Edoardo; Vulic, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020
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
URL: https://doi.org/10.17863/CAM.62209
https://www.repository.cam.ac.uk/handle/1810/315102
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11
Specializing unsupervised pretraining models for word-level semantic similarity
Ponti, Edoardo Maria; Korhonen, Anna; Vulić, Ivan. - : Association for Computational Linguistics, ACL, 2020
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12
Classification-based self-learning for weakly supervised bilingual lexicon induction
Vulić, Ivan; Korhonen, Anna; Glavaš, Goran. - : Association for Computational Linguistics, 2020
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13
Probing pretrained language models for lexical semantics
Vulić, Ivan; Korhonen, Anna; Litschko, Robert. - : Association for Computational Linguistics, 2020
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14
XCOPA: A multilingual dataset for causal commonsense reasoning
Ponti, Edoardo Maria; Majewska, Olga; Liu, Qianchu. - : Association for Computational Linguistics, 2020
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15
Improving bilingual lexicon induction with unsupervised post-processing of monolingual word vector spaces
Glavaš, Goran; Korhonen, Anna; Vulić, Ivan. - : Association for Computational Linguistics, 2020
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16
SemEval-2020 Task 2: Predicting multilingual and cross-lingual (graded) lexical entailment
Glavaš, Goran; Vulić, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2020
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17
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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18
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ...
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19
Multilingual and cross-lingual graded lexical entailment
Glavaš, Goran; Vulić, Ivan; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2019
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20
How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
Glavaš, Goran; Litschko, Robert; Ruder, Sebastian. - : Association for Computational Linguistics, 2019
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