What can linguistics and deep learning contribute to each other? Response to Pater
Bibliographic Data
| ID | 7900194 |
|---|---|
| Authors | Tal Linzen (0000-0003-0435-6912, corresponding author) |
| Year | 2019 |
| Volume | 95 |
| Issue | 1 |
| Pages | e99-e108 |
| Publication date | 2019-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Language (JOURNAL) |
| Journal identifiers | ISSN: 0097-8507 • E-ISSN: 1535-0665 |
| Publisher | Project MUSE (PUBLISHER • US) |
| DOI | 10.1353/lan.2019.0015 |
| OpenAlex | W2916562859 |
| Language | EN |
| Citations received | 11 |
| References cited | 6 |
Joe Pater's (2019) target article calls for greater interaction between neural network research and linguistics. I expand on this call and show how such interaction can benefit both fields. Linguists can contribute to research on neural networks for language technologies by clearly delineating the linguistic capabilities that can be expected of such systems, and by constructing controlled experimental paradigms that can determine whether those desiderata have been met. In the other direction, neural networks can benefit the scientific study of language by providing infrastructure for modeling human sentence processing and for evaluating the necessity of particular innate constraints on language acquisition
Applied linguistics · Artificial neural network · Cognitive science · Comprehension approach · Human language · Language acquisition · Language technology · Linguistics · Natural language · Natural language processing · Sentence · Sentence processing · Artificial Intelligence · Computer Science · Natural Language Processing Techniques · Neurobiology of Language and Bilingualism · Philosophy · Psychology · Topic Modeling
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| Unique citing works | 11 |
|---|---|
| Citations per year | 2,2 |
| Citation span | 2021 - 2025 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 9 |