Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning

Anonymous

Paper Link: https://openreview.net/forum?id=y7mbZumiRFQ

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract: Commonsense reasoning systems should be able to generalize to diverse reasoning cases. However, most state-of-the-art approaches depend on expensive data annotations and overfit to a specific benchmark without learning how to perform general semantic reasoning. To overcome these drawbacks, zero-shot QA systems have shown promise as a robust learning scheme by transforming a commonsense knowledge graph (KG) into synthetic QA-form samples for model training. Considering the increasing type of different commonsense KGs, this paper aims to extend the zero-shot transfer learning scenario into multiple-source settings, where different KGs can be utilized synergetically. Towards this goal, we propose to mitigate the loss of knowledge from the interference among the different knowledge sources, by developing a modular variant of the knowledge aggregation as a new zero-shot commonsense reasoning framework. Results on five commonsense reasoning benchmarks demonstrate the efficacy of our framework, improving the performance with multiple KGs.

Copyright Consent Signature (type Name Or NA If Not Transferrable): Yu Jin Kim

Copyright Consent Name And Address: Yu Jin Kim, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul, 03722, Republic of Korea