Graph4IUR: Incomplete Utterance Rewriting with Semantic Graph

Abstract

Utterance rewriting aims to identify and supply the omitted information in human conversation, which further enables the downstream task to understand conversations more comprehensively. Recently, sequence edit methods, which leverage the overlap between two sentences, have been widely applied to narrow the search space confronted by the previous linear generation methods. However, these methods ignore the relationship between linguistic elements in the conversation, which reflects how the knowledge and thoughts are organized in human communication. In this case, although most of the content in rewritten sentences can be found in the context, we found that some connecting words expressing relationships are often missing, which results in the out-of-context problem for the previous sentence edit method. To that end, in this paper, we propose a new semantic Graph-based Incomplete Utterance Rewriting (Graph4IUR) framework, which takes the semantic graph to depict the relationship between linguistic elements and captures out-of-context words. Specifically, we adopt the Abstract Meaning Representation (AMR) [4] graph as the basic sentence-to-graph method to depict the dialogue from the graph perspective, which could well represent the high-level semantics relationships of sentences. Along this line, we further adapt the sentence editing models to rewrite without changing the sentence architecture, which brings a restriction to exploring the overlap part of the current and rewritten sentences in the IUR task. Extensive experimental results indicate that our Graph4IUR framework can effectively alleviate the out-of-context problem and improve the performance of the previous edit-based methods in the IUR task.