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UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation
- Publication Year :
- 2022
- Publisher :
- arXiv, 2022.
-
Abstract
- Question answering requiring discrete reasoning, e.g., arithmetic computing, comparison, and counting, over knowledge is a challenging task. In this paper, we propose UniRPG, a semantic-parsing-based approach advanced in interpretability and scalability, to perform unified discrete reasoning over heterogeneous knowledge resources, i.e., table and text, as program generation. Concretely, UniRPG consists of a neural programmer and a symbolic program executor, where a program is the composition of a set of pre-defined general atomic and higher-order operations and arguments extracted from table and text. First, the programmer parses a question into a program by generating operations and copying arguments, and then the executor derives answers from table and text based on the program. To alleviate the costly program annotation issue, we design a distant supervision approach for programmer learning, where pseudo programs are automatically constructed without annotated derivations. Extensive experiments on the TAT-QA dataset show that UniRPG achieves tremendous improvements and enhances interpretability and scalability compared with state-of-the-art methods, even without derivation annotation. Moreover, it achieves promising performance on the textual dataset DROP without derivations.<br />Comment: Accepted to EMNLP 2022
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....99bc0ac911fcbe64b499eedd0043a34e
- Full Text :
- https://doi.org/10.48550/arxiv.2210.08249