mlsanityData to 14 Oct 2026

Answering Conversational Questions on Structured Data without Logical Forms

arXiv 1908.11787 ↗First posted 30 Aug 2019cs.CLcited by 24

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Abstract (arXiv)

We present a novel approach to answering sequential questions based on structured objects such as knowledge bases or tables without using a logical form as an intermediate representation. We encode tables as graphs using a graph neural network model based on the Transformer architecture. The answers are then selected from the encoded graph using a pointer network. This model is appropriate for processing conversations around structured data, where the attention mechanism that selects the answers to a question can also be used to resolve conversational references. We demonstrate the validity of this approach with competitive results on the Sequential Question Answering (SQA) task (Iyyer et al., 2017).

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Aug 2019.

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Citing papers per year: 2021: 4; 2022: 8; 2023: 4; 2024: 2; 2025: 6.

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  1. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 235
  2. 2024TableBench: A Comprehensive and Complex Benchmark for Table Question Answeringcited by 128
  3. 2022TableFormer: Robust Transformer Modeling for Table-Text Encodingcited by 91
  4. 2022Neural Approaches to Conversational Information Retrievalcited by 42
  5. 2022A Survey on Table Question Answering: Recent Advancescited by 41
  6. 2025Table-R1: Region-based Reinforcement Learning for Table Understandingcited by 37
  7. 2023HYTREL: Hypergraph-enhanced Tabular Data Representation Learningcited by 36
  8. 2021Conversational Question Answering: A Surveycited by 31
  9. 2022Conversational Question Answering on Heterogeneous Sourcescited by 26
  10. 2021Representations for Question Answering from Documents with Tables and Textcited by 21
  11. 2022Table-To-Text generation and pre-training with TabT5cited by 20
  12. 2021Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphscited by 18
12 more
  1. 2022DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articlescited by 17
  2. 2023From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuningcited by 14
  3. 2025RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysiscited by 12
  4. 2022Interactive Question Answering Systems: Literature Reviewcited by 7
  5. 2025Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understandingcited by 7
  6. 2025From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNscited by 6
  7. 2022TableQuery: Querying tabular data with natural languagecited by 5
  8. 2025Document Intelligence in the Era of Large Language Models: A Surveycited by 5
  9. 2023Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrievalcited by 2
  10. 2025How Accurate Are LLMs at Multi-Question Answering on Conversational Transcripts?cited by 2
  11. 2024Training Table Question Answering via SQL Query Decompositioncited by 1
  12. 2023Around the GLOBE: Numerical Aggregation Question-Answering on Heterogeneous Genealogical Knowledge Graphs with Deep Neural Networkscited by 0