Papers with WebQuestionsSP
Semantic Structure Based Query Graph Prediction for Question Answering over Knowledge Graph (2022.coling-1)
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| Challenge: | Existing approaches for query graph generation ignore the semantic structure of a question . Existing methods ignore the structure of the question, resulting in noisy query graph candidates. |
| Approach: | They propose to build query graphs from natural language questions to predict semantic structure of a question. |
| Outcome: | The proposed method can predict the semantic structure of a question using six semantic structures from common questions in KGQA. |
Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge (2021.naacl-main)
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| Challenge: | Past research has shown that large neural language models encode surprising amounts of factual information, but augmenting or modifying this information requires modifying a corpus and retraining, which is computationally expensive. |
| Approach: | They propose a neural LM that includes an interpretable neuro-symbolic KB in the form of a "fact memory" their LM improves performance on knowledge-intensive question-answering tasks, sometimes dramatically . |
| Outcome: | The proposed model improves on knowledge-intensive question-answering tasks, including a 27 point increase in one setting of WebQuestionsSP over a state-of-the-art open-book model. |
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases (2022.coling-1)
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| Challenge: | Existing methods train one encoder-decoder-based model to fit all questions . however, such a one-size-fits-all strategy may not perform well for complex questions involving multiple KB relations or functional constraints. |
| Approach: | They propose a meta-learning framework for complex question generation over knowledge bases . they propose he meta-trained generator can acquire universal meta-knowledge . |
| Outcome: | The proposed framework can acquire universal and transferable meta-knowledge and quickly adapt to long-tailed samples under different dimensions. |
TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Base (2022.emnlp-main)
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| Challenge: | KBQA is a challenging area for pre-trained language models due to its extensive space and complexity. |
| Approach: | They propose a model that uses multi-grained retrieval to focus on most relevant KB contexts . constrained decoding is used to control output space and reduce generation errors . |
| Outcome: | The proposed model outperforms existing models on GrailQA and WebQuestionsSP. |
Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models (2024.acl-long)
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| Challenge: | Knowledge base question answering (KBQA) is a challenging task, particularly in parsing intricate questions into executable logical forms. |
| Approach: | They propose a framework to generate logical forms through direct interaction with knowledge bases (KBs) by annotating a dataset with step-wise reasoning processes. |
| Outcome: | The proposed framework achieves competitive results on the WebQuestionsSP, ComplexWebQuestIONS, KQA Pro, and MetaQA datasets with a minimal number of examples (shots). Importantly, the proposed model supports manual intervention, allowing for the iterative refinement of LLM outputs. |
Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection (2021.emnlp-main)
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| Challenge: | Existing models that handle single-entity questions have focused on relation following . introducing intersection improves performance on multiple-entities questions by over 14% . |
| Approach: | They propose a model that explicitly handles multiple-entity questions by implementing an intersection operation. |
| Outcome: | The proposed model improves on multiple-entity questions by over 14% on two datasets . it also improves performance on questions with multiple entities by 19% . |