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Generalizable Neuro-symbolic Systems for Commonsense Question Answering

arXiv 2201.06230 ↗First posted 17 Jan 2022cs.CL cs.AIcited by 3

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Abstract (arXiv)

This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks. Different methods for integrating neural language models and knowledge graphs are discussed. The situations in which this combination is most appropriate are characterized, including quantitative evaluation and qualitative error analysis on a variety of commonsense question answering benchmark datasets.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 40 of its 66 references that are in the map. First posted on arXiv 17 Jan 2022.

Builds on

40 of 66 references.

References · 40

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28 more
  1. 2019Abductive Commonsense Reasoningcited by 300
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  3. 2019KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoningcited by 216
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  16. 2019Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answeringcited by 37
  17. 2020Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoningcited by 33
  18. 2021Core Challenges in Embodied Vision-Language Planningcited by 30
  19. 2020Self-supervised Knowledge Triplet Learning for Zero-shot Question Answeringcited by 29
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  22. 2019Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answeringcited by 16
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  28. 2021On the Capabilities of Pointer Networks for Deep Deductive Reasoningcited by 4

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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2024Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-makingcited by 5
  2. 2023Rethinking the Evaluating Framework for Natural Language Understanding in AI Systems: Language Acquisition as a Core for Future Metricscited by 2
  3. 2023A Novel Neural-symbolic System under Statistical Relational Learningcited by 1