A Sequence Modelling Approach to Question Answering in Text-Based Games

Furman, Greg and Toledo, Edan and Shock, Jonathan and Buys, Jan (2022) A Sequence Modelling Approach to Question Answering in Text-Based Games, Proceedings of Wordplay: When Language Meets Games Workshop, 14 July 2022, Association for Computational Linguistics.

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Abstract

Interactive Question Answering (IQA) requires an intelligent agent to interact with a dynamic environment in order to gather information necessary to answer a question. IQA tasks have been proposed as means of training systems to develop language or visual comprehension abilities. To this end, the Question Answering with Interactive Text (QAit) task was created to produce and benchmark interactive agents capable of seeking information and answering questions in unseen environments. While prior work has exclusively focused on IQA as a reinforcement learning problem, such methods suffer from low sample efficiency and poor accuracy in zero-shot evaluation. In this paper, we propose the use of the recently proposed Decision Transformer architecture to provide improvements upon prior baselines. By utilising a causally masked GPT-2 Transformer for command generation and a BERT model for question answer prediction, we show that the Decision Transformer achieves performance greater than or equal to current state-of-the-art RL baselines on the QAit task in a sample efficient manner. In addition, these results are achievable by training on sub-optimal random trajectories, therefore not requiring the use of online agents to gather data.

Item Type:	Conference paper
Subjects:	Computing methodologies > Artificial intelligence > Natural language processing Computing methodologies > Machine learning > Learning paradigms > Reinforcement learning > Sequential decision making
Date Deposited:	15 Nov 2022 08:07
Last Modified:	15 Nov 2022 08:07
URI:	https://pubs.cs.uct.ac.za/id/eprint/1548

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