How Additional Knowledge can Improve Natural Language Commonsense Question Answering?
This work addresses the challenge of enhancing question-answering accuracy in domains requiring commonsense knowledge, though it appears incremental as it builds on existing language models and datasets.
The paper tackled the problem of improving natural language commonsense question answering by incorporating external knowledge sources into language models, and demonstrated that performance improves with such integration.
Recently several datasets have been proposed to encourage research in Question Answering domains where commonsense knowledge is expected to play an important role. Recent language models such as ROBERTA, BERT and GPT that have been pre-trained on Wikipedia articles and books have shown reasonable performance with little fine-tuning on several such Multiple Choice Question-Answering (MCQ) datasets. Our goal in this work is to develop methods to incorporate additional (commonsense) knowledge into language model-based approaches for better question-answering in such domains. In this work, we first categorize external knowledge sources, and show performance does improve on using such sources. We then explore three different strategies for knowledge incorporation and four different models for question-answering using external commonsense knowledge. We analyze our predictions to explore the scope of further improvements.