Towards Controlled and Diverse Generation of Article Comments

TL;DR

This paper introduces a novel system for controllable and diverse article comment generation, enabling explicit emotion control and improved diversity through hierarchical copying and restricted beam search.

Contribution

It presents a new approach combining emotion control, hierarchical copying, and restricted beam search to generate more expressive and diverse comments.

Findings

High accuracy in emotion expression

Enhanced comment diversity

Effective control over comment content

Abstract

Much research in recent years has focused on automatic article commenting. However, few of previous studies focus on the controllable generation of comments. Besides, they tend to generate dull and commonplace comments, which further limits their practical application. In this paper, we make the first step towards controllable generation of comments, by building a system that can explicitly control the emotion of the generated comments. To achieve this, we associate each kind of emotion category with an embedding and adopt a dynamic fusion mechanism to fuse this embedding into the decoder. A sentence-level emotion classifier is further employed to better guide the model to generate comments expressing the desired emotion. To increase the diversity of the generated comments, we propose a hierarchical copy mechanism that allows our model to directly copy words from the input articles. We also propose a restricted beam search (RBS) algorithm to increase intra-sentence diversity. Experimental results show that our model can generate informative and diverse comments that express the desired emotions with high accuracy.