Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation

Abstract

Multi-modal dialog modeling is of growing interest. In this work, we propose frameworks to resolve a specific case of multi-modal dialog generation that better mimics multi-modal dialog generation in the real world, where each dialog turn is associated with the visual context in which it takes place. Specifically, we propose to model the mutual dependency between text-visual features, where the model not only needs to learn the probability of generating the next dialog utterance given preceding dialog utterances and visual contexts, but also the probability of predicting the visual features in which a dialog utterance takes place, leading the generated dialog utterance specific to the visual context. We observe significant performance boosts over vanilla models when the mutual dependency between text and visual features is modeled. 1

ConRPG: Paraphrase Generation using Contexts as Regularizer

“…For two sentences of the same meaning, the probability of generating contexts given the two sentences should be also the same, which correspond to the backward probability given from sentences to contexts. This is akin to the bi-directional mutual-information based generation strategy (Fang et al, 2015;Li et al, 2016a;Li and Jurafsky, 2016;Wang et al, 2021). The backward probability can be modeled by predicting preceding contexts given subsequent contexts p(c <i |c i , c >i ) and to predict subsequent contexts given preceding contexts p(c >i |c <i , c i ).…”