Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement Learning

• URL: http://arxiv.org/abs/2110.00842v1
• Date: Sat, 2 Oct 2021 16:58:26 GMT
• Title: Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement Learning
• Authors: Sayan Ghosh and Shashank Srivastava
• Abstract summary: We pose program generation from language as Inverse Reinforcement Learning. Fine-tuning with our approach achieves significantly better performance than competitive methods using Reinforcement Learning (RL) Generated programs are also preferred by human evaluators over an RL-based approach, and rated higher on relevance, completeness, and human-likeness.
• Score: 12.107259467873092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mapping natural language instructions to programs that computers can process is a fundamental challenge. Existing approaches focus on likelihood-based training or using reinforcement learning to fine-tune models based on a single reward. In this paper, we pose program generation from language as Inverse Reinforcement Learning. We introduce several interpretable reward components and jointly learn (1) a reward function that linearly combines them, and (2) a policy for program generation. Fine-tuning with our approach achieves significantly better performance than competitive methods using Reinforcement Learning (RL). On the VirtualHome framework, we get improvements of up to 9.0% on the Longest Common Subsequence metric and 14.7% on recall-based metrics over previous work on this framework (Puig et al., 2018). The approach is data-efficient, showing larger gains in performance in the low-data regime. Generated programs are also preferred by human evaluators over an RL-based approach, and rated higher on relevance, completeness, and human-likeness.

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