13 December 2022, Computational Linguistics Seminar, Tejaswini Deoskar

In this talk, I will discuss ongoing research on several topics, on the general theme of generalisation in natural language models. First, I will talk about the generalisation problem in analytically complex syntactic parsers, where it is necessary to go beyond supervised models, for instance for parsing out-of-domain data or low-resource languages; specifically I will present recent results on constructing complex category types (in CCG or other categorial grammars) that are unseen in the training data, on-the-fly. Second, I will discuss a use-case for a syntactic parser applied to a new domain: detecting syntactic markers of “agency” in language use. Loss of agency is correlated to psychological conditions like depression or fatigue syndrome, and often expressed in the language produced by patients (e.g. in excessive use of passives). Automatic detection of such markers can help medical professionals intervene and predict recovery in online treatments. Third, I will discuss recent research on incorporating image-external knowledge for “contextualised” image-captioning: here we develop a generalisable system that can identify broad-coverage external knowledge relevant to an image. The system can generate informative as well as factually-correct captions, and be applied to various image-language scenarios.