Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation

Abstract

Open-domain dialogue generation in natural language processing (NLP) is by default a pure-language task, which aims to satisfy human need for daily communication on open-ended topics by producing related and informative responses. In this paper, we point out that hidden images, named as visual impressions (VIs), can be explored from the text-only data to enhance dialogue understanding and help generate better responses. Besides, the semantic dependency between an dialogue post and its response is complicated, e.g., few word alignments and some topic transitions. Therefore, the visual impressions of them are not shared, and it is more reasonable to integrate the response visual impressions (RVIs) into the decoder, rather than the post visual impressions (PVIs). However, both the response and its RVIs are not given directly in the test process. To handle the above issues, we propose a framework to explicitly construct VIs based on pure-language dialogue datasets and utilize them for better dialogue understanding and generation. Specifically, we obtain a group of images (PVIs) for each post based on a pre-trained word-image mapping model. These PVIs are used in a co-attention encoder to get a post representation with both visual and textual information. Since the RVIs are not provided directly during testing, we design a cascade decoder that consists of two sub-decoders. The first sub-decoder predicts the content words in response, and applies the word-image mapping model to get those RVIs. Then, the second sub-decoder generates the response based on the post and RVIs. Experimental results on two open-domain dialogue datasets show that our proposed approach achieves superior performance over competitive baselines. CCS CONCEPTS• Computing methodologies → Discourse, dialogue and pragmatics; Natural language generation;

Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization

“…However, the generated responses may suffer from some notorious problems, such as being generic, inconsistent, or unrelated to the given contexts. Previous studies tried to solve these issues by feeding extra information, e.g., topics [25], sentence types [36], personas [19], emotions [43,60], documents [30], multi-modal [46] or knowledge [17,27,55,56,61], augmenting the model itself [54,59], or modifying the loss function [18].…”