CRGA: Homographic pun detection with a contextualized-representation: Gated attention network

摘要

Detecting a homographic pun is one of the fundamental research tasks in natural language processing. A homographic pun is able to produce humor through the latent relationship between the pun and its semantically similar target. Puns have been widely applied in written and spoken forms of human language and have a long history. However, the ambiguity of a homographic pun is still a large challenge that cannot be addressed well with current methods. To alleviate this problem, we present a novel contextualized-representation gated attention (CRGA) network for the detection of homographic puns. This architecture has several advantages that can be exploited: one is that a contextual representation is used across varying linguistic contexts to address the polysemy of homographic puns; another other is that the CRGA model is able to detect homographic puns by combining the global semantic understanding, local script understanding, pun characteristic self-attention and gated mechanism. With this design, the CRGA model can effectively capture the polysemy information, which is helpful for homographic pun detection. The experimental results based on the common SemEval2017 Task7 and Pun of the Day datasets demonstrate the effectiveness and advancement of our proposed CRGA model.