Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge

TL;DR

This paper introduces a novel pivot-oriented deep selection model (PoDS) that enhances multi-turn dialogue reading comprehension by selecting key turns and integrating external knowledge, significantly improving performance on benchmark tasks.

Contribution

It is the first to combine pivot turn selection with external knowledge injection for dialogue comprehension, addressing noise and knowledge gaps.

Findings

Achieves significant improvements on four benchmark datasets.

Demonstrates the effectiveness of pivot turn selection in dialogue understanding.

Shows that external knowledge injection further boosts comprehension accuracy.

Abstract

Multi-turn dialogue reading comprehension aims to teach machines to read dialogue contexts and solve tasks such as response selection and answering questions. The major challenges involve noisy history contexts and especial prerequisites of commonsense knowledge that is unseen in the given material. Existing works mainly focus on context and response matching approaches. This work thus makes the first attempt to tackle the above two challenges by extracting substantially important turns as pivot utterances and utilizing external knowledge to enhance the representation of context. We propose a pivot-oriented deep selection model (PoDS) on top of the Transformer-based language models for dialogue comprehension. In detail, our model first picks out the pivot utterances from the conversation history according to the semantic matching with the candidate response or question, if any. Besides, knowledge items related to the dialogue context are extracted from a knowledge graph as external knowledge. Then, the pivot utterances and the external knowledge are combined with a well-designed mechanism for refining predictions. Experimental results on four dialogue comprehension benchmark tasks show that our proposed model achieves great improvements on baselines. A series of empirical comparisons are conducted to show how our selection strategies and the extra knowledge injection influence the results.