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A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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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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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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The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures ...
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces ...
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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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73 |
Probing Pretrained Language Models for Lexical Semantics ...
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Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction ...
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75 |
Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces ...
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76 |
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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Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces
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Abstract:
Work on projection-based induction of cross-lingual word embedding spaces (CLWEs) predominantly focuses on the improvement of the projection (i.e., mapping) mechanisms. In this work, in contrast, we show that a simple method for post-processing monolingual embedding spaces facilitates learning of the cross-lingual alignment and, in turn, substantially improves bilingual lexicon induction (BLI). The post-processing method we examine is grounded in the generalisation of first- and second-order monolingual similarities to the nth-order similarity. By post-processing monolingual spaces before the cross-lingual alignment, the method can be coupled with any projection-based method for inducing CLWE spaces. We demonstrate the effectiveness of this simple monolingual post-processing across a set of 15 typologically diverse languages (i.e., 15*14 BLI setups), and in combination with two different projection methods.
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URL: https://doi.org/10.17863/CAM.53927 https://www.repository.cam.ac.uk/handle/1810/306836
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The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
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Lauscher, Anne; Vulic, Ivan; Ponti, Edoardo. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.118, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020
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