A Dual-Attention Neural Network for Pun Location and Using Pun-Gloss Pairs for Interpretation

TL;DR

This paper introduces DANN, a dual-attention neural network that simultaneously locates puns and interprets their meanings by integrating word senses, pronunciation, and context, achieving state-of-the-art results on benchmark datasets.

Contribution

The paper presents a novel dual-attention neural network model that jointly addresses pun location and interpretation, improving over previous isolated approaches.

Findings

Achieves new state-of-the-art results on benchmark datasets.

Effectively integrates word senses, pronunciation, and context.

Treats pun interpretation as a classification task using pun-gloss pairs.

Abstract

Pun location is to identify the punning word (usually a word or a phrase that makes the text ambiguous) in a given short text, and pun interpretation is to find out two different meanings of the punning word. Most previous studies adopt limited word senses obtained by WSD(Word Sense Disambiguation) technique or pronunciation information in isolation to address pun location. For the task of pun interpretation, related work pays attention to various WSD algorithms. In this paper, a model called DANN (Dual-Attentive Neural Network) is proposed for pun location, effectively integrates word senses and pronunciation with context information to address two kinds of pun at the same time. Furthermore, we treat pun interpretation as a classification task and construct pungloss pairs as processing data to solve this task. Experiments on the two benchmark datasets show that our proposed methods achieve new state-of-the-art results. Our source code is available in the public code repository.