DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge

TL;DR

This paper introduces DISCOS, a framework that automatically converts discourse knowledge from ASER into large-scale, high-quality commonsense knowledge similar to ATOMIC, significantly improving coverage and diversity without extra annotation.

Contribution

DISCOS is a novel framework that bridges discourse knowledge and commonsense knowledge, enabling automatic, large-scale, high-quality knowledge acquisition from discourse graphs.

Findings

Successfully converted discourse knowledge into 3.4 million inferential commonsense facts.

Outperforms previous supervised methods in novelty and diversity of knowledge.

Achieves comparable quality with significantly larger coverage.

Abstract

Commonsense knowledge is crucial for artificial intelligence systems to understand natural language. Previous commonsense knowledge acquisition approaches typically rely on human annotations (for example, ATOMIC) or text generation models (for example, COMET.) Human annotation could provide high-quality commonsense knowledge, yet its high cost often results in relatively small scale and low coverage. On the other hand, generation models have the potential to automatically generate more knowledge. Nonetheless, machine learning models often fit the training data well and thus struggle to generate high-quality novel knowledge. To address the limitations of previous approaches, in this paper, we propose an alternative commonsense knowledge acquisition framework DISCOS (from DIScourse to COmmonSense), which automatically populates expensive complex commonsense knowledge to more affordable linguistic knowledge resources. Experiments demonstrate that we can successfully convert discourse knowledge about eventualities from ASER, a large-scale discourse knowledge graph, into if-then commonsense knowledge defined in ATOMIC without any additional annotation effort. Further study suggests that DISCOS significantly outperforms previous supervised approaches in terms of novelty and diversity with comparable quality. In total, we can acquire 3.4M ATOMIC-like inferential commonsense knowledge by populating ATOMIC on the core part of ASER. Codes and data are available at https://github.com/HKUST-KnowComp/DISCOS-commonsense.