mlsanityData to 7 Oct 2026

Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks

arXiv 2104.01569 ↗First posted 4 Apr 2021cs.CLcited by 24

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Abstract (arXiv)

This paper addresses the task of (complex) conversational question answering over a knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks). It is the first approach, which employs a transformer architecture extended with Graph Attention Networks for multi-task neural semantic parsing. LASAGNE uses a transformer model for generating the base logical forms, while the Graph Attention model is used to exploit correlations between (entity) types and predicates to produce node representations. LASAGNE also includes a novel entity recognition module which detects, links, and ranks all relevant entities in the question context. We evaluate LASAGNE on a standard dataset for complex sequential question answering, on which it outperforms existing baseline averages on all question types. Specifically, we show that LASAGNE improves the F1-score on eight out of ten question types; in some cases, the increase in F1-score is more than 20% compared to the state of the art.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 4 Apr 2021.

Builds on

16 of 27 references.

References · 16

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2017Graph Attention Networkscited by 8,854
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  7. 2015Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Modelscited by 207
  8. 2017Variational Reasoning for Question Answering with Knowledge Graphcited by 170
  9. 2018Neural Approaches to Conversational AIcited by 164
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  11. 2016Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervisioncited by 101
  12. 2018Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graphcited by 67
4 more
  1. 2019Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansioncited by 42
  2. 2019Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Basecited by 33
  3. 2020Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Modelscited by 22
  4. 2020MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalitiescited by 10

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Citing papers per year: 2021: 4; 2022: 8; 2023: 9; 2024: 2; 2025: 1.

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  1. 2023Document Understanding Dataset and Evaluation (DUDE)cited by 132
  2. 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generationcited by 67
  3. 2022Cross-modal Prototype Driven Network for Radiology Report Generationcited by 58
  4. 2021Complex Knowledge Base Question Answering: A Surveycited by 52
  5. 2022SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generationcited by 34
  6. 2021Conversational Question Answering: A Surveycited by 31
  7. 2022SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understandingcited by 28
  8. 2022Conversational Question Answering on Heterogeneous Sourcescited by 26
  9. 2022Unifying Data Perspectivism and Personalization: An Application to Social Normscited by 22
  10. 2021Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphscited by 10
  11. 2023Semantic Parsing for Conversational Question Answering over Knowledge Graphscited by 10
  12. 2023Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generationcited by 7
12 more
  1. 2024Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphscited by 7
  2. 2023GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answeringcited by 6
  3. 2022GraphWOZ: Dialogue Management with Conversational Knowledge Graphscited by 5
  4. 2023History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modelingcited by 4
  5. 2023Learning to Select the Relevant History Turns in Conversational Question Answeringcited by 4
  6. 2021VOGUE: Answer Verbalization through Multi-Task Learningcited by 3
  7. 2023UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Promptcited by 3
  8. 2022Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphscited by 2
  9. 2023Conversational Semantic Parsing using Dynamic Context Graphscited by 2
  10. 2022An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphscited by 1
  11. 2025A Survey of the State-of-the-Art in Conversational Question Answering Systemscited by 1
  12. 2023Learning Section Weights for Multi-Label Document Classificationcited by 0