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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; Litschko, Robert. - : 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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Abstract:
Current methods of cross-lingual parser transfer focus on predicting the best parser for a low-resource target language globally, that is, "at treebank level”. In this work, we propose and argue for a novel cross-lingual transfer paradigm: instance-level parser selection (ILPS), and present a proof-of-concept study focused on instance-level selection in the framework of delexicalized parser transfer. Our work is motivated by an empirical observation that different source parsers are the best choice for different Universal POS-sequences (i.e., UPOS sentences) in the target language. We then propose to predict the best parser at the instance level. To this end, we train a supervised regression model, based on the Transformer architecture, to predict parser accuracies for individual POS-sequences. We compare ILPS against two strong single-best parser selection baselines (SBPS): (1) a model that compares POS n-gram distributions between the source and target languages (KL) and (2) a model that selects the source based on the similarity between manually created language vectors encoding syntactic properties of languages (L2V). The results from our extensive evaluation, coupling 42 source parsers and 20 diverse low-resource test languages, show that ILPS outperforms KL and L2V on 13/20 and 14/20 test languages, respectively. Further, we show that by predicting the best parser “at treebank level” (SBPS), using the aggregation of predictions from our instance-level model, we outperform the same baselines on 17/20 and 16/20 test languages.
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URL: https://doi.org/10.17863/CAM.62214 https://www.repository.cam.ac.uk/handle/1810/315107
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From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers
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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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