Tabular and textual question answering requires systems to perform reasoning over heterogeneous information, considering table structure, and the connections among table and text. In this paper, we propose a ChAin-centric Reasoning and Pre-training framework (CARP). CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering. We also propose a novel chain-centric pre-training method, to enhance the pre-trained model in identifying the cross-modality reasoning process and alleviating the data sparsity problem. This method constructs the large-scale reasoning corpus by synthesizing pseudo heterogeneous reasoning paths from Wikipedia and generating corresponding questions. We evaluate our system on OTT-QA, a large-scale table-and-text open-domain question answering benchmark, and our system achieves the state-of-the-art performance. Further analyses illustrate that the explicit hybrid chain offers substantial performance improvement and interpretablity of the intermediate reasoning process, and the chain-centric pre-training boosts the performance on the chain extraction.
Yale University（耶鲁大学）；Zhejiang University（浙江大学）；Penn State University（宾夕法尼亚州立大学）
Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills. Current models with table-specific architectures and pre-training methods perform well on understanding table structures, but they still struggle with tasks that require various table reasoning skills. In this work, we develop ReasTAP to show that high-level table reasoning skills can be injected into models during pre-training without a complex table-specific architecture design. We define 7 table reasoning skills, such as numerical operation, temporal comparison, and conjunction…
Document-Level Numerical Reasoning across Single and Multiple Tables in Financial Reports
Department of Computer Science and Information Engineering, National Taiwan University（国立台湾大学资讯工程学系）；FinTech Center, National Taiwan University（国立台湾大学金融科技中心）
TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering
Tokyo Institute of Technology（东京工业大学）；Nanyang Technological University（南洋理工大学）；Microsoft Research Asia（微软亚洲研究院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Reasoning by Commented Code for Table Question Answering
Department of Data Science, Seoul National University, Seoul, Republic of Korea（数据科学系，首尔国立大学，首尔，大韩民国）；Department of Computer Science（计算机科学系）；Engineering, Seoul National University, Seoul, Republic of Korea（工程系，首尔国立大学，首尔，大韩民国）
Institute of Automation, CAS（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
In the real-world question answering scenarios, hybrid form combining both tabular and textual contents has attracted more and more attention, among which numerical reasoning problem is one of the most typical and challenging problems. Existing methods usually adopt encoder-decoder framework to represent hybrid contents and generate answers. However, it can not capture the rich relationship among numerical value, table schema, and text information on the encoder side. The decoder uses a simple predefined operator classifier which is not flexible enough to handle numerical reasoning processes w…
Harbin Institute of Technology（哈尔滨工业大学）；JD AI Research（京东人工智能研究院）
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 a…
Department of Computer Science ETH Zurich（苏黎世联邦理工学院计算机科学系）；ETH Zurich（苏黎世联邦理工学院）；Switzerland（瑞士）
Advances in natural language processing tasks have gained momentum in recent years due to the increasingly popular neural network methods. In this paper, we explore deep learning techniques for answering multi-step reasoning questions that operate on semi-structured tables. Challenges here arise from the level of logical compositionality expressed by questions, as well as the domain openness. Our approach is weakly supervised, trained on question-answer-table triples without requiring intermediate strong supervision. It performs two phases: first, machine understandable logical forms (programs…
S$^3$HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Department of Computer Science, The University of Manchester（曼彻斯特大学计算机科学系）
In this paper, we describe a dataset and baseline result for a question answering that utilizes web tables. It contains commonly asked questions on the web and their corresponding answers found in tables on websites. Our dataset is novel in that every question is paired with a table of a different signature. In particular, the dataset contains two classes of tables: entity-instance tables and the key-value tables. Each QA instance comprises a table of either kind, a natural language question, and a corresponding structured SQL query. We build our model by dividing question answering into sever…
Research Center for Social Computing and Information Retrieval（社会计算与信息检索研究中心）；Harbin Institute of Technology（哈尔滨工业大学）
Table-and-text hybrid question answering (HybridQA) is a widely used and challenging NLP task commonly applied in the financial and scientific domain. The early research focuses on migrating other QA task methods to HybridQA, while with further research, more and more HybridQA-specific methods have been present. With the rapid development of HybridQA, the systematic survey is still under-explored to summarize the main techniques and advance further research. So we present this work to summarize the current HybridQA benchmarks and methods, then analyze the challenges and future directions of th…
Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Search
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合国家科学中心人工智能研究院）
MATA: Multi-Agent Framework for Reliable and Flexible Table Question Answering
Department of Electrical and Computer Engineering, Seoul National University（首尔国立大学电气与计算机工程系）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔国立大学人工智能跨学科项目）
University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）
Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information alone might lead to severe coverage problems. To fill in the gap, we present HybridQA https://github.com/wenhuchen/HybridQA, a new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are …
Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective
University of Science and Technology of China（中国科学技术大学）；The University of Melbourne（墨尔本大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合国家科学中心人工智能研究院）
Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
University of Science and Technology of China（中国科学技术大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）
In this paper, we introduce SciGen, a new challenge dataset for the task of reasoning-aware data-to-text generation consisting of tables from scientific articles and their corresponding descriptions. Describing scientific tables goes beyond the surface realization of the table content and requires reasoning over table values. The unique properties of SciGen are that (1) tables mostly contain numerical values, and (2) the corresponding descriptions require arithmetic reasoning. SciGen is therefore the first dataset that assesses the arithmetic reasoning capabilities of generation models on comp…
University of Waterloo（滑铁卢大学）；Vector Institute（向量研究所）
Recent literature has shown that large language models (LLMs) are generally excellent few-shot reasoners to solve text reasoning tasks. However, the capability of LLMs on table reasoning tasks is yet to be explored. In this paper, we aim at understanding how well LLMs can perform table-related tasks with few-shot in-context learning. Specifically, we evaluated LLMs on popular table QA and fact verification datasets like WikiTableQuestion, FetaQA, TabFact, and FEVEROUS and found that LLMs are competent at complex reasoning over table structures, though these models are not pre-trained on any ta…
Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding
University of Science and Technology of China（中国科学技术大学）；Alibaba Group（阿里巴巴集团）；Singapore University of Technology and Design（新加坡科技设计大学）；Shanghai Jiao Tong University（上海交通大学）
When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables
University of Science and Technology of China（中国科学技术大学）；The University of Melbourne（墨尔本大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
