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XCOPA: A multilingual dataset for causal commonsense reasoning
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Improving bilingual lexicon induction with unsupervised post-processing of monolingual word vector spaces
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SemEval-2020 Task 2: Predicting multilingual and cross-lingual (graded) lexical entailment
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02425462 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (3), pp.559-601. ⟨10.1162/coli_a_00357⟩ ; https://www.mitpressjournals.org/doi/abs/10.1162/coli_a_00357 (2019)
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
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Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines ...
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ...
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A neural classification method for supporting the creation of BioVerbNet ...
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A neural classification method for supporting the creation of BioVerbNet ...
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Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation ...
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A neural classification method for supporting the creation of BioVerbNet ...
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Second-order contexts from lexical substitutes for few-shot learning of word representations ...
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A Neural Classification Method for Supporting the Creation of BioVerbNet ...
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Enhancing biomedical word embeddings by retrofitting to verb clusters ...
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A Neural Classification Method for Supporting the Creation of BioVerbNet
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Second-order contexts from lexical substitutes for few-shot learning of word representations
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Abstract:
There is a growing awareness of the need to handle rare and unseen words in word representation modelling. In this paper, we focus on few-shot learning of emerging concepts that fully exploits only a few available contexts. We introduce a substitute-based context representation technique that can be applied on an existing word embedding space. Previous context-based approaches to modelling unseen words only consider bag-of-word firstorder contexts, whereas our method aggregates contexts as second-order substitutes that are produced by a sequence-aware sentence completion model. We experimented with three tasks that aim to test the modelling of emerging concepts. We found that these tasks show different emphasis on first and second order contexts, and our substitute-based method achieved superior performance on naturallyoccurring contexts from corpora.
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URL: https://doi.org/10.17863/CAM.44054 https://www.repository.cam.ac.uk/handle/1810/297013
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Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation
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A neural classification method for supporting the creation of BioVerbNet
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