4 accepted papers
Common image-text joint understanding techniques presume that images and the associated text can universally be characterized by a single implicit model. However, co-occurring images and text can be related in qualitatively different ways, and explicitly modeling it could improve the performance of…
Developers of text generation models rely on automated evaluation metrics as a stand-in for slow and expensive manual evaluations. However, image captioning metrics have struggled to give accurate learned estimates of the semantic and pragmatic success of output text. We address this weakness by int…
The interpretation of the lexical aspect of verbs in English plays a crucial role in tasks such as recognizing textual entailment and learning discourse-level inferences. We show that two elementary dimensions of aspectual class, states vs. events, and telic vs. atelic events, can be modelled effect…
In many domains, dialogue systems need to work collaboratively with users to successfully reconstruct the meaning the user had in mind. In this paper, we show how cognitive models of users’ communicative strategies can be leveraged in a reinforcement learning approach to dialogue planning to enable…