Graph-Based Knowledge Integration for Question Answering over Dialogue (2020)

Abstract: Question answering over dialogue, a specialized machine reading comprehension task, aims to comprehend a dialogue and to answer specific questions. Despite many advances, existing approaches for this task did not consider dialogue structure and background knowledge (e.g., relationships between speakers). In this paper, we introduce a new approach for the task, featured by its novelty in structuring dialogue and integrating background knowledge for reasoning. Specifically, different from previous “structure-less” approaches, our method organizes a dialogue as a “relational graph”, using edges to represent relationships between entities. To encode this relational graph, we devise a relational graph convolutional network (R-GCN), which can traverse the graph’s topological structure and effectively encode multi-relational knowledge for reasoning. The extensive experiments have justified the effectiveness of our approach over competitive baselines. Moreover, a deeper analysis shows that our model is better at tackling complex questions requiring relational reasoning and defending adversarial attacks with distracting sentences.

## introduction humans obtain information by engaging in conversations. question answering (qa) over dialogue, a specialized machine reading comprehension (mrc) task @xcite , aims to test the ability of a qa system to comprehend a dialogue, by asking it to answer questions about the dialogue. consider the example shown in ## approach figure 1 schematically visualizes our approach, which involves three major steps: in the following illustrations, let @xmath0, u 2 , ..., u n } be a dialogue with n utterances. each utterance u i is associated with a speaker s i . the texts in u i can be denoted as {w i1 , w i2 , ..., w im } where w ij is the jth token in u i , and m is the length of u i . let a question be @xmath1, q 2 , . . . , q l } where l is the length of q. given d and q, qa over dialogue requires to predict an answer a. note a is restricted to be a (continuous) span in d (yang and choi, 2019). ## experimental results the results of comparing our approach with baselines models are shown in ## discussion we further investigate the performance of our model on different types of questions and conduct a robustness testing to understand why our approach is effective. ## case study we study several cases by comparing the difference in predictions of bert pre and our approach. the results are shown in