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Spatial multi-arrangement for clustering and multi-way similarity dataset construction
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Majewska, Olga; McCarthy, D; van den Bosch, J. - : European Language Resources Association, 2020. : LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings, 2020
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Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis
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Majewska, Olga; Vulic, Ivan; McCarthy, Diana. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.423, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment
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Glavas, Goran; Vulic, Ivan; Korhonen, Anna-Leena. - : International Committee for Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.semeval-1.2, 2020. : Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), 2020
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Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction
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Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers
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Glavas, Goran; Agic, Zeljko; Vulic, Ivan. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.345, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020
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From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers
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Abstract:
Massively multilingual transformers (MMTs) pretrained via language modeling (e.g., mBERT, XLM-R) have become a default paradigm for zero-shot language transfer in NLP, offering unmatched transfer performance. Current evaluations, however, verify their efficacy in transfers (a) to languages with sufficiently large pretraining corpora, and (b) between close languages. In this work, we analyze the limitations of downstream language transfer with MMTs, showing that, much like cross-lingual word embeddings, they are substantially less effective in resource-lean scenarios and for distant languages. Our experiments, encompassing three lower-level tasks (POS tagging, dependency parsing, NER) and two high-level tasks (NLI, QA), empirically correlate transfer performance with linguistic proximity between source and target languages, but also with the size of target language corpora used in MMT pretraining. Most importantly, we demonstrate that the inexpensive few-shot transfer (i.e., additional fine-tuning on a few target-language instances) is surprisingly effective across the board, warranting more research efforts reaching beyond the limiting zero-shot conditions.
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URL: https://doi.org/10.17863/CAM.62210 https://www.repository.cam.ac.uk/handle/1810/315103
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Emergent Communication Pretraining for Few-Shot Machine Translation
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Vulic, Ivan; Ponti, Edoardo; Korhonen, Anna. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.416, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020
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MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
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SemEval-2020 Task 3: Graded Word Similarity in Context
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Santos Armendariz, Carlos; Purver, Matthew; Pollak, Senja. - : International Committee for Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.semeval-1.3, 2020. : Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), 2020
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Multidirectional Associative Optimization of Function-Specific Word Representations
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Gerz, Daniela; Vulic, Ivan; Rei, Marek. - : Association for Computational Linguistics, 2020. : 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), 2020
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AdapterHub: A Framework for Adapting Transformers
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Pfeiffer, Jonas; Ruckle, Andreas; Poth, Clifton. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP 2020), 2020
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XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
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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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Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations
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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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