ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering (2022.coling-1)
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| Challenge: | Existing ranking-based KBQA models struggle with flexibility in predicting complicated queries and have impractical running time. |
| Approach: | They propose a new generation-based question answering on knowledge bases model that addresses both large search space and ambiguities in schema linking. |
| Outcome: | The proposed model overcomes two intertwined challenges on popular KBQA datasets and is highly competitive and efficient. |
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| Challenge: | Existing methods for knowledge base question answering lack grammaticality, faithfulness, and controllability due to hallucinations in the reasoning process. |
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| Challenge: | Knowledge base question answering (KBQA) is a challenging task, particularly in parsing intricate questions into executable logical forms. |
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| Challenge: | Existing methods for Knowledge Base Question Answering (KBQA) face hallucination problems, resulting in low accuracy. |
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Sumit Neelam, Udit Sharma, Hima Karanam, Shajith Ikbal, Pavan Kapanipathi, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Young-Suk Lee, Santosh Srivastava, Cezar Pendus, Saswati Dana, Dinesh Garg, Achille Fokoue, G P Shrivatsa Bhargav, Dinesh Khandelwal, Srinivas Ravishankar, Sairam Gurajada, Maria Chang, Rosario Uceda-Sosa, Salim Roukos, Alexander Gray, Guilherme Lima, Ryan Riegel, Francois Luus, L V Subramaniam
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