Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

Abstract

Recently, natural language processing (NLP) has had increasing success and produced extensive industrial applications. Despite being sufficient to enable these applications, current NLP systems often ignore the social part of language, e.g., who says it, in what context, for what goals. In this talk, we take a closer look at social factors in language via a new theory taxonomy, and its interplay with computational methods via two lines of work. The first one studies what makes language persuasive by introducing a semi-supervised method to leverage hierarchical structures in text to recognize persuasion strategies in good-faith requests. The second part demonstrates how various structures in conversations can be utilized to generate better summaries for everyday interaction. We conclude by discussing several open-ended questions towards how to build socially aware language technologies, with the hope of getting closer to the goal of human-like language understanding.