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Hits 41 – 52 of 52

41
Speakers Fill Lexical Semantic Gaps with Context
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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42
Predicting Declension Class from Form and Meaning
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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43
A Tale of a Probe and a Parser
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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44
A Corpus for Large-Scale Phonetic Typology
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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45
Phonotactic Complexity and Its Trade-offs
In: Transactions of the Association for Computational Linguistics, 8 (2020)
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46
Information-Theoretic Probing for Linguistic Structure
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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47
Metaphor Detection Using Context and Concreteness
In: Proceedings of the Second Workshop on Figurative Language Processing (2020)
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48
Pareto Probing: Trading Off Accuracy for Complexity ...
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49
A Tale of a Probe and a Parser ...
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50
A Tale of a Probe and a Parser ...
Abstract: Measuring what linguistic information is encoded in neural models of language has become popular in NLP. Researchers approach this enterprise by training "probes" - supervised models designed to extract linguistic structure from another model's output. One such probe is the structural probe (Hewitt and Manning, 2019), designed to quantify the extent to which syntactic information is encoded in contextualised word representations. The structural probe has a novel design, unattested in the parsing literature, the precise benefit of which is not immediately obvious. To explore whether syntactic probes would do better to make use of existing techniques, we compare the structural probe to a more traditional parser with an identical lightweight parameterisation. The parser outperforms structural probe on UUAS in seven of nine analysed languages, often by a substantial amount (e.g. by 11.1 points in English). Under a second less common metric, however, there is the opposite trend - the structural probe outperforms ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2005.01641
https://dx.doi.org/10.48550/arxiv.2005.01641
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51
Predicting Declension Class from Form and Meaning ...
Williams, Adina; Pimentel, Tiago; Blix, Hagen. - : ETH Zurich, 2020
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52
Rethinking Phonotactic Complexity
In: Proceedings of the Society for Computation in Linguistics (2019)
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