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Automated speech tools for helping communities process restricted-access corpora for language revival efforts ...
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Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions ...
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Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages ...
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Sensitivity as a Complexity Measure for Sequence Classification Tasks ...
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Concord begets concord: A Bayesian model of nominal concord typology
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In: Proceedings of the Linguistic Society of America; Vol 6, No 1 (2021): Proceedings of the Linguistic Society of America; 541–555 ; 2473-8689 (2021)
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Universals of word order reflect optimization of grammars for efficient communication.
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In: Proceedings of the National Academy of Sciences of the United States of America, vol 117, iss 5 (2020)
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A Framework for the Computational Linguistic Analysis of Dehumanization ...
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Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models ...
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The Role of Verb Semantics in Hungarian Verb-Object Order ...
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Predicting Declension Class from Form and Meaning
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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| 15 |
The Paradigm Discovery Problem
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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| 16 |
A Tale of a Probe and a Parser
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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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 the parser. This begs the question: which metric should we prefer?
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URL: https://doi.org/10.3929/ethz-b-000462303
https://hdl.handle.net/20.500.11850/462303
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| 17 |
A Corpus for Large-Scale Phonetic Typology
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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Information-Theoretic Probing for Linguistic Structure
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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| 19 |
It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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