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常识问答研究综述

Summary

This survey, written in Chinese, covers commonsense question answering (CQA). In CQA a system must answer natural-language questions using everyday knowledge that the input text does not state. It can do this by choosing among options, judging a statement true or false, or generating an answer. The authors trace the field in two stages. The first relied on hand-built rules and features, as in early Winograd-style work. The second uses neural networks and pre-trained language models. They split modern approaches into two families: models that learn to sense commonsense from knowledge sources, and models that inject separately encoded knowledge into the answering pipeline. They also compare knowledge resources by how each was built, where its content comes from, and how it is represented. From this they propose a six-type taxonomy of the knowledge that questions require. A dedicated section looks at combining knowledge with large language models, and the authors run experiments on cross-dataset transfer. The provided excerpt omits the numeric results of those experiments. The survey is useful as a structured map of datasets, knowledge sources and fusion techniques, and it offers baselines for building generalizable CQA systems.

Key points

  • Task scope and field size: The task is split into discriminative CQA (multiple choice, true/false) and generative CQA. The authors count 12 established datasets drawn from 9 domains, and they identify 4 research sub-directions:
    • building knowledge sources,
    • acquiring knowledge,
    • fusion-based reasoning,
    • explainable generation.
  • Datasets examined in detail (9): CommonsenseQA, OpenbookQA, ARC, Social IQA, Cosmos QA, MCScript, MCScript2.0, ReCoRD and ProtoQA. Earlier benchmarks also discussed are COPA (2011), WSC (2012), SCT and SWAG.
  • Knowledge taxonomy: The proposed scheme has six categories: attribute, semantic, causal, context, abstract and intention.
  • Representative systems reviewed:
    • Feature-based and rule-based WSC solvers using FrameNet and WordNet;
    • Self-talk (GPT/GPT-2);
    • ERNIE 3.0;
    • knowledge-driven pre-training on Atomic, ConceptNet, WordNet, Visual Genome and Wikidata;
    • KagNet (ConceptNet paths of up to 4 hops);
    • heterogeneous ConceptNet+Wikipedia graph reasoning;
    • Headhunter (attention-based filtering of OMCS knowledge).
  • Open challenges and citation: The authors highlight three remaining problems: retrieving implicit knowledge, aligning structured knowledge with text, and the fact that even very large models (GPT3, T5) still fall short of practical reliability. For citation, the paper appeared in Journal of Software, vol. 35(1), 2024; it was first published online in August 2023.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages · Natural language processing

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.13328/j.cnki.jos.006913

@article{2023,
  title = {{常识问答研究综述}},
  author = {范怡帆 and 邹博伟 and 徐庆婷 and 李志峰 and 洪宇},
  journal = {软件学报 (Journal of Software)},
  year = {2023},
  doi = {10.13328/j.cnki.jos.006913},
  url = {https://doi.org/10.13328/j.cnki.jos.006913}
}

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