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Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine. ...
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BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine.
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XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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Cross-lingual semantic specialization via lexical relation induction ...
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Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization ...
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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Do we really need fully unsupervised cross-lingual embeddings? ...
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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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Probing Pretrained Language Models for Lexical Semantics ...
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On the relation between linguistic typology and (limitations of) multilingual language modeling ...
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The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures ...
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Spatial multi-arrangement for clustering and multi-way similarity dataset construction ...
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Cross-lingual semantic specialization via lexical relation induction
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Ponti, Edoardo; Vulić, I; Glavaš, G. - : EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2020
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On the relation between linguistic typology and (limitations of) multilingual language modeling
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Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization
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The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures
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Abstract:
Performance in cross-lingual NLP tasks is impacted by the (dis)similarity of languages at hand: e.g., previous work has suggested there is a connection between the expected success of bilingual lexicon induction (BLI) and the assumption of (approximate) isomorphism between monolingual embedding spaces. In this work we present a large-scale study focused on the correlations between monolingual embedding space similarity and task performance, covering thousands of language pairs and four different tasks: BLI, parsing, POS tagging and MT. We hypothesize that statistics of the spectrum of each monolingual embedding space indicate how well they can be aligned. We then introduce several isomorphism measures between two embedding spaces, based on the relevant statistics of their individual spectra. We empirically show that 1) language similarity scores derived from such spectral isomorphism measures are strongly associated with performance observed in different cross-lingual tasks, and 2) our spectral-based measures consistently outperform previous standard isomorphism measures, while being computationally more tractable and easier to interpret. Finally, our measures capture complementary information to typologically driven language distance measures, and the combination of measures from the two families yields even higher task performance correlations.
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URL: https://doi.org/10.17863/CAM.62208 https://www.repository.cam.ac.uk/handle/1810/315101
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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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