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Bridging the divide between linguistics and natural language processing: From vectors to symbols and back again| title | Bridging the divide between linguistics and natural language processing: From vectors to symbols and back again |
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| start_date | 2025/06/11 |
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| schedule | 16h30-17h30 |
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| online | no |
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| location_info | sur Zoom |
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| summary | Capturing the structure of language is a central goal in both linguistics and natural language processing (NLP). Despite this overlap in goals, linguistics and NLP differ substantially in how they handle language. Linguistic theories typically assume a symbolic view, in which discrete units (e.g., words) are combined in structured ways (e.g., in syntax trees). In NLP, however, the most successful systems are neural networks, which encode information in continuous vectors that do not appear to have any of the rich structure posited in linguistics. In this talk, I will discuss two lines of work that show how vectors and symbols may not be as incompatible as they seem. First, I will present analyses of how standard neural networks represent syntactic structure, with the conclusion that, in at least many cases, these models build implicit symbolic representations. Thus, despite appearances, they may not be incompatible with symbolic views of language. Second, I will turn from representations to learning by showing how neural networks can realize strong, structured learning biases of the sort traditionally seen only in symbolic models; the experiments in this part of the talk are made possible by recent advances in meta-learning. Taken together, these results provide some first steps toward bridging the divide between vectors and symbols. |
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| responsibles | Bernard |
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| submitted | published | | 2025/07/02 06:08 UTC |
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