NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering
Hybrid tabular-textual question answering (QA) requires reasoning from heterogeneous information, and the types of reasoning are mainly divided into numerical reasoning and span extraction. Current numerical reasoning methods autoregressively decode program sequences, and each decoding step produces either an operator or an operand. However, the step-by-step decoding suffers from exposure bias, and the accuracy of program generation drops sharply as the decoding steps unfold due to error propagation. In this paper, we propose a non-autoregressive program generation framework, which independently generates complete program tuples containing both operators and operands, can address the error propagation issue while significantly boosting the speed of program generation. Experiments on the ConvFinQA and MultiHiertt datasets show that our non-autoregressive program generation method can bring about substantial improvements over the strong FinQANet (+5.06 Exe Acc and +4.80 Prog Acc points) and MT2Net (+7.97 EM and +6.38 F1 points) baselines, establishing the new state-of-the-art performance, while being much faster (21x) in program generation. Finally, with increasing numbers of numerical reasoning steps the performance drop of our method is significantly smaller than that of the baselines. Our code will be publicly available soon.
Code (0)
등록된 구현이 없습니다.
Tasks
Question AnsweringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data
Autoregressive models are predominant in natural language generation, while their application in tabular data remains underexplored. We posit that this can be attributed to two factors: 1) tabular data contains heterogen…
Text GenerationDP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation
The generation of synthetic tabular data that preserves differential privacy is a problem of growing importance. While traditional marginal-based methods have achieved impressive results, recent work has shown that deep …
Deep LearningTabular Data GenerationSnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training
Existing text-to-image (T2I) diffusion models face several limitations, including large model sizes, slow runtime, and low-quality generation on mobile devices. This paper aims to address all of these challenges by devel…
Knowledge DistillationText-to-Image GenerationTableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning
Retrieval-Augmented Generation (RAG) has demonstrated considerable effectiveness in open-domain question answering. However, when applied to heterogeneous documents, comprising both textual and tabular components, existi…
Answer GenerationChunkingOpen-Domain Question AnsweringQuestion Answering+4SnapGuard: Lightweight Prompt Injection Detection for Screenshot-Based Web Agents
Web agents have emerged as an effective paradigm for automating interactions with complex web environments, yet remain vulnerable to prompt injection attacks that embed malicious instructions into webpage content to indu…