Entity Resolution in Open-domain Conversations (2021)

Abstract: In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest. To improve the relevancy of retrieved knowledge, we propose a neural entity linking (NEL) approach. Different from formal documents, such as news, conversational utterances are informal and multi-turn, which makes it more challenging to disambiguate the entities. Therefore, we present a context-aware named entity recognition model (NER) and entity resolution (ER) model to utilize dialogue context information. We conduct NEL experiments on three open-domain conversation datasets and validate that incorporating context information improves the performance of NER and ER models. The end-to-end NEL approach outperforms the baseline by 62.8% relatively in F1 metric. Furthermore, we verify that using external knowledge based on NEL benefits the neural response generation model.

## introduction building an informative open-domain conversational agent that can naturally interact with humans has been one of recent scientific research topics. inspired by the development of neural networks, neural generation based conversation systems have made great progress @xcite @xcite @xcite . however, one issue in such approaches is that the neural models often produce universal and less informative responses @xcite . to address this issue, previous work proposed to incorporate external information into the response generation models, such as topics @xcite and emotions @xcite . one line of research investigates the use of external knowledge to enrich the information of the responses @xcite @xcite @xcite . ## conclusion in this paper, we investigate nel in multi-turn open-domain conversations. considering the characteristic of dialogs, where the meaning of the cur-rent utterance often varies depending on the context, we design a context-aware ner model and an er model. experimental results on three datasets prove that using context information improves the entity recognition and resolution performance. extrinsic evaluation on response generation also validates the effectiveness of the entity information.