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Probing Language Models for Paraphrastic Representations of Negation and Antonymy

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If you have a question about this talk, please contact Panagiotis Fytas .

The ability of large language models to represent essential linguistic properties makes them applicable across a wide range of tasks. However, it is still an open question how well they address systematic compositionality and to what level of abstraction they reflect the actual meaning behind a given sentence. I present work on constructing a dedicated challenge set focusing on a specific linguistic phenomenon. In particular, the challenge set assesses the ability of language models to properly represent paraphrasticity realised in expressions that incorporate antonymy and negation.

I present a large-scale evaluation of publicly available pre-trained language models on the proposed challenge set, showing large variation between models fine-tuned for different downstream tasks.

This talk is part of the Language Technology Lab Seminars series.

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CR-UK CRI Machine learning theory Lecture Theatre 6 (LT6), Department of Engineering

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