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ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering

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arxiv 2204.08109 v3 pith:J6C3LZXC submitted 2022-04-17 cs.CL

Yu Gu , Yu Su This is my paper · ORCID
classification cs.CL
keywords searchspacearcaneqadynamickbqalargelinkingschema
verification ladder T0 review T1 audit T2 compute T3 formal

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Question answering on knowledge bases (KBQA) poses a unique challenge for semantic parsing research due to two intertwined challenges: large search space and ambiguities in schema linking. Conventional ranking-based KBQA models, which rely on a candidate enumeration step to reduce the search space, struggle with flexibility in predicting complicated queries and have impractical running time. In this paper, we present ArcaneQA, a novel generation-based model that addresses both the large search space and the schema linking challenges in a unified framework with two mutually boosting ingredients: dynamic program induction for tackling the large search space and dynamic contextualized encoding for schema linking. Experimental results on multiple popular KBQA datasets demonstrate the highly competitive performance of ArcaneQA in both effectiveness and efficiency.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering

    cs.CL 2025-07 reject novelty 6.0 of 10

    BYOKG-RAG combines LLM-generated entities, paths, queries, and candidate answers with multiple graph retrieval tools to answer questions over custom knowledge graphs without training data.

  2. Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering

    cs.CL 2025-05 conditional novelty 6.0 of 10

    RTSoG combines question decomposition, LLM-reward-guided Monte Carlo Tree Search with a self-critic stop signal, and weighted path stacking to achieve new state-of-the-art KGQA accuracy, though without code or error bars.

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