Compositional Semantics Network With Multi-Task Learning for Pun Location

Junyu Mao

doi:10.1109/ACCESS.2020.2978208

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Compositional Semantics Network With Multi-Task Learning for Pun Location

2020-01-01

DOI：10.1109/ACCESS.2020.2978208

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Abstract

En 中文

A pun is always humorous and has strong interactive value in people's daily communication. It creates a humorous effect in a certain context, in which a word implies two or more meanings by using polysemy (homographic pun) or phonological similarity to another word (heterographic pun). Pun location is a task to identify the pun word in a given text, which is of great significance to understand humorous texts. Existing methods generally adopt single long sequence structure but cannot well capture the rich semantics of pun words in sentences. We present an approach that considers long-distance and short-distance semantic relations between words simultaneously. For the long-distance semantic relation, we introduce multi-level embeddings to represent the most relevant aspects of the data. For the short-distance semantic relation, we exploit the complex-valued model with a self-adaptive selection mechanism based on multi-scale of input information. Meanwhile, we propose a new classification task to distinguish the homographic pun and heterographic pun. We introduce it as an auxiliary to jointly train the original pun location task, which first learns the location of different types of puns together. Experiment results show that the latest state-of-the-art results can be achieved through our model.

Keywords:

Semantics

Task analysis

Feature extraction

Licenses

Training

Bidirectional control

Encoding

Pun location

quantum theory

multi-task learning

attention mechanism

deep learning

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