Papers with question understanding
Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding (2022.naacl-main)
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| Challenge: | Temporal reading comprehension (TRC) is a natural way to study temporal relations since natural language questions are flexible to capture divergent temporal relationships. |
| Approach: | They propose a reading comprehension approach that uses precise question understanding . they embed a temporal ordering question into two vectors and evaluate the temporal relation based on that . |
| Outcome: | The proposed approach outperforms strong baselines and achieves state-of-the-art performance on the TORQUE dataset. |
MMCoQA: Conversational Question Answering over Text, Tables, and Images (2022.acl-long)
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| Challenge: | Existing conversational QA systems only use a single knowledge source, e.g., paragraphs or knowledge graph, and assume it contains enough evidence to extract answers to users' questions. |
| Approach: | They propose a task to answer users' questions with multimodal knowledge sources via multi-turn conversations using a multimodal dataset. |
| Outcome: | The proposed task brings a series of research challenges, including but not limited to priority, consistency, and complementarity of multimodal knowledge. |
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering (2021.findings-acl)
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Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-Suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Gangi Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
| Challenge: | Existing approaches face challenges including complex question understanding and lack of large end-to-end training datasets. |
| Approach: | They propose a modular knowledge base question answering system that leverages AMR parses for task-independent question understanding. |
| Outcome: | The proposed system achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia. |
Teaching Small Language Models to Reason for Knowledge-Intensive Multi-Hop Question Answering (2024.findings-acl)
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| Challenge: | Large language models can teach small language models to solve complex reasoning tasks by Chain-of-thought Distillation (CoTD) e.g., mathematical question answering. |
| Approach: | They propose a method that distills two student models to solve a multi-hop question . they use chain-of-thought distillation to generate step-by-step reasoning paths . |
| Outcome: | The proposed method surpasses existing methods on knowledge-intensive multi-hop questions. |
HTML: Hierarchical Topology Multi-task Learning for Semantic Parsing in Knowledge Base Question Answering (2025.findings-acl)
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| Challenge: | Existing approaches struggle with mapping questions to precise logical forms . Existing frameworks struggle with complex mapping of questions to logical form . |
| Approach: | They propose a framework that leverages a hierarchical multi-task learning paradigm to enhance the performance of logical form generation. |
| Outcome: | The proposed framework outperforms supervised fine-tuning methods and training-free ones on large language models. |
XLQA: A Benchmark for Locale-Aware Multilingual Open-Domain Question Answering (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown significant progress in Open-domain question answering (ODQA) but most evaluations focus on English and assume locale-invariant answers across languages. |
| Approach: | They propose a benchmark specifically designed for locale-sensitive multilingual ODQA that uses 3,000 English seed questions expanded to eight languages. |
| Outcome: | The proposed benchmarks are based on 3,000 English seed questions expanded to eight languages and a human-verified annotation distinguishing locale-invariant and locale-sensitive cases. |