Automatic Acrostic Couplet Generation with Three-Stage Neural Network Pipelines

TL;DR

This paper presents a three-stage neural network pipeline for automatic acrostic couplet generation, incorporating attention-based models and clustering techniques, validated by BLEU scores, human evaluation, and deployment on WeChat.

Contribution

It introduces a novel three-stage neural pipeline with clustering-based beam search for acrostic couplet generation, enhancing semantic relatedness and diversity.

Findings

BLEU scores demonstrate improved quality.

Human judgments confirm effectiveness.

System deployed on WeChat for real users.

Abstract

As one of the quintessence of Chinese traditional culture, couplet compromises two syntactically symmetric clauses equal in length, namely, an antecedent and subsequent clause. Moreover, corresponding characters and phrases at the same position of the two clauses are paired with each other under certain constraints of semantic and/or syntactic relatedness. Automatic couplet generation is recognized as a challenging problem even in the Artificial Intelligence field. In this paper, we comprehensively study on automatic generation of acrostic couplet with the first characters defined by users. The complete couplet generation is mainly divided into three stages, that is, antecedent clause generation pipeline, subsequent clause generation pipeline and clause re-ranker. To realize semantic and/or syntactic relatedness between two clauses, attention-based Sequence-to-Sequence (S2S) neural network is employed. Moreover, to provide diverse couplet candidates for re-ranking, a cluster-based beam search approach is incorporated into the S2S network. Both BLEU metrics and human judgments have demonstrated the effectiveness of our proposed method. Eventually, a mini-program based on this generation system is developed and deployed on Wechat for real users.