FEGI: A Fusion Extractive-Generative Model for Dialogue Ellipsis and Coreference Integrated Resolution

Dialogue systems in open domain have achieved great success due to the easily obtained single-turn corpus and the development of deep learning, but the multi-turn scenario is still a challenge because of the frequent coreference and information omission. In this paper, we aim to quickly retrieve the omitted or coreferred expressions contained in history dialogue and restore them into the incomplete utterance. Jointly inspired by the generative method for text generation and extractive method for span extraction, we propose a fusion extractive-generative dialogue ellipsis and coreference integrated resolution model(FEGI). In detail, we introduce two training tasks OMIT and SPAN to extract missing semantic expressions, then integrate the expressions obtained into the decoding initial and copy stages of the generative model respectively. To support the training tasks, we introduce an algorithm for secondary reconstruction annotation based on existing publicly available corpora via unsupervised technique, which can work in cases of no annotation of the missing semantic expressions. Moreover, We conduct dozens of joint learning experiments on the CamRest676 and RiSAWOZ datasets. Experimental results show that our proposed model significantly outperforms the state-of-the-art models in terms of quality.