30 August 2022, Computational Linguistics Seminar, Alberto Testoni

Recent years have witnessed an explosion of NLP models for many different tasks, both in text-only and multimodal (vision & language) settings. Impressive results have been obtained on multimodal encoders, whereas decoders have received less attention. In my work, I focus on the latter aiming to study the problem-solving reasoning behind natural language generation. To this end, I take referential grounded dialogue games as a testbed. I will discuss the main issues affecting generative systems and explore how the weaknesses of the encoder affect the choice of the decoder by focusing on the interpretation of negatively answered questions. I will then present a cognitively-inspired re-ranking decoding strategy for promoting the generation of strategic questions. I will compare this strategy to a wide variety of different decoding algorithms proposed in the literature, together with an in-depth analysis of their hyper-parameter configurations. Finally, I will briefly mention some ongoing works on exploring how modeling human uncertainty can lead to better natural language generation systems and an investigation of pragmatic phenomena that allow humans to efficiently solve referential games.