Abstract：Entity relation extraction aims to extract semantic relations between entities from text. This task is well addressed for normative texts such as news reports and Wikipedia, but less touched for dialogue texts. Compared with standard text, dialogue is an interactive process, and such information hidden in the interaction challenges the entity relation extraction task. This paper proposes an entity relation extraction method that incorporates interactive information via cross-attention mechanisms. It also adopt the multitask learning to deal with the issue of unblance distribution. The experiments on DialogRE public dataset reveal a result of 54.1% F1 and 50.7% of F1c, which proves validity of the method.
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2021, Vol. 35 
