UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation

• URL: http://arxiv.org/abs/2210.08249v1
• Date: Sat, 15 Oct 2022 10:17:52 GMT
• Title: UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation
• Authors: Yongwei Zhou, Junwei Bao, Chaoqun Duan, Youzheng Wu, Xiaodong He, Tiejun Zhao
• Abstract summary: We propose UniRPG, a semantic-parsing-based approach advanced in interpretability and scalability. UniRPG performs unified discrete reasoning over heterogeneous knowledge resources, i.e., table and text, as program generation. It achieves tremendous improvements and enhances interpretability and scalability compared with state-of-the-art methods.
• Score: 32.74302320558048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• NoviCode: Generating Programs from Natural Language Utterances by Novices [59.71218039095155]
  We present NoviCode, a novel NL Programming task which takes as input an API and a natural language description by a novice non-programmer. We show that NoviCode is indeed a challenging task in the code synthesis domain, and that generating complex code from non-technical instructions goes beyond the current Text-to-Code paradigm.
  arXiv  Detail & Related papers  (2024-07-15T11:26:03Z)
• H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables [56.73919743039263]
  This paper introduces a novel algorithm that integrates both symbolic and semantic (textual) approaches in a two-stage process to address limitations. Our experiments demonstrate that H-STAR significantly outperforms state-of-the-art methods across three question-answering (QA) and fact-verification datasets.
  arXiv  Detail & Related papers  (2024-06-29T21:24:19Z)
• Case-Based Reasoning Approach for Solving Financial Question Answering [5.10832476049103]
  FinQA introduced a numerical reasoning dataset for financial documents. We propose a novel approach to tackle numerical reasoning problems using case based reasoning (CBR) Our model retrieves relevant cases to address a given question, and then generates an answer based on the retrieved cases and contextual information.
  arXiv  Detail & Related papers  (2024-05-18T10:06:55Z)
• GPT is becoming a Turing machine: Here are some ways to program it [16.169056235216576]
  We show that GPT-3 models can be triggered to execute programs that involve loops. We show that prompts that may not even cover one full task example can trigger algorithmic behaviour.
  arXiv  Detail & Related papers  (2023-03-25T00:43:41Z)
• Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Framework [5.351873055148804]
  Self-training framework generates diverse synthetic data with complex logic. We optimize the procedure using a "Table-Text Manipulator" to handle joint table-text reasoning scenarios. UCTRST achieves above 90% of the supervised model performance on different tasks and domains.
  arXiv  Detail & Related papers  (2022-12-20T09:15:03Z)
• A Conversational Paradigm for Program Synthesis [110.94409515865867]
  We propose a conversational program synthesis approach via large language models. We train a family of large language models, called CodeGen, on natural language and programming language data. Our findings show the emergence of conversational capabilities and the effectiveness of the proposed conversational program synthesis paradigm.
  arXiv  Detail & Related papers  (2022-03-25T06:55:15Z)
• ProTo: Program-Guided Transformer for Program-Guided Tasks [59.34258016795216]
  We formulate program-guided tasks which require learning to execute a given program on the observed task specification. We propose the Program-guided Transformer (ProTo), which integrates both semantic and structural guidance of a program. ProTo executes a program in a learned latent space and enjoys stronger representation ability than previous neural-symbolic approaches.
  arXiv  Detail & Related papers  (2021-10-02T13:46:32Z)
• Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language [81.73820295186727]
  We propose a formalism of procedures as programs, a powerful yet intuitive method of representing hierarchical procedural knowledge for agent command and control. We instantiate this framework on the IQA and ALFRED datasets for NL instruction following.
  arXiv  Detail & Related papers  (2021-09-16T20:36:21Z)
• Understanding Unnatural Questions Improves Reasoning over Text [54.235828149899625]
  Complex question answering (CQA) over raw text is a challenging task. Learning an effective CQA model requires large amounts of human-annotated data. We address the challenge of learning a high-quality programmer (parser) by projecting natural human-generated questions into unnatural machine-generated questions.
  arXiv  Detail & Related papers  (2020-10-19T10:22:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.