A Tale of a Probe and a Parser
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Date
2020-07
Publication Type
Conference Paper
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yes
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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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published
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Book title
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Journal / series
Volume
Pages / Article No.
7389 - 7395
Publisher
Association for Computational Linguistics
Event
58th Annual Meeting of the Association-for-Computational-Linguistics (ACL 2020) (virtual)
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Software
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Organisational unit
09682 - Cotterell, Ryan / Cotterell, Ryan
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.