NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset (2021.findings-emnlp)
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| Challenge: | Existing question answering datasets lack numerical reasoning and reasoning processes . current research on numerical reasoning focuses on simple calculations . |
| Approach: | They propose a conversational and bilingual question answering dataset with numerical reasoning with compound mathematical expressions. |
| Outcome: | The proposed model achieves 55.5 exact match scores while human performance is 89.7. |
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Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, Christopher D. Manning
| Challenge: | Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. |
| Approach: | They propose a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) the questions provide sentence-level supporting facts required for reasoning; and (4) a type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. |
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Chart Question Answering from Real-World Analytical Narratives (2025.acl-srw)
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| Challenge: | a dataset for chart question answering is constructed from visualization notebooks . data visualizations are an essential modality for communicating complex information about data. |
| Approach: | They propose a dataset for chart question answering constructed from visualization notebooks . they use real-world, multi-view charts paired with natural language questions . |
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MarkQA: A large scale KBQA dataset with numerical reasoning (2023.emnlp-main)
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| Challenge: | Existing KBQA datasets are insufficient for numerical reasoning . existing KBqa datasets lack multi-hop reasoning and numerical reasoning. |
| Approach: | They propose a task that necessitates the ability to perform multi-hop reasoning and numerical reasoning. |
| Outcome: | The proposed task necessitates the ability to perform multi-hop reasoning and numerical reasoning. |
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering (2021.naacl-main)
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| Challenge: | Existing question answering systems lack the ability to access relevant knowledge and reason over it. |
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Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya (2023.acl-long)
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| Challenge: | Question-Answering (QA) has seen significant advances in recent years, achieving near human-level performance over some benchmarks. |
| Approach: | They propose to use a native QA dataset for an East African language, Tigrinya, to build similar resources for related languages. |
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JEMHopQA: Dataset for Japanese Explainable Multi-Hop Question Answering (2024.lrec-main)
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| Challenge: | a dataset for explainable QA in Japanese is available for many languages, but not in other languages. |
| Approach: | They present a multi-hop QA dataset based on Japanese Wikipedia . it includes question-answer pairs and supporting evidence in the form of derivation triples . they show that the dataset is sufficiently challenging for state-of-the-art LLMs based upon this dataset . |
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DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning (2024.findings-acl)
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| Challenge: | Question answering (QA) is a fundamental task in the field of Natural Language Processing (NLP). |
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RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering (2023.findings-emnlp)
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| Challenge: | Existing QA models are not robust to variations in question constraints, but can be made more robust by tuning on clusters of related questions. |
| Approach: | They introduce RoMQA, the first benchmark for robust, multi-evidence, multianswer question answering (QA) RoMQ contains clusters of related questions that are derived from the Wikidata knowledge graph . |
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Intrinsic Subgraph Generation for Interpretable Graph Based Visual Question Answering (2024.lrec-main)
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| Challenge: | Visual Question Answering (VQA) is acknowledged as a challenging multi-modal task for Machine Learning (ML). |
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Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis (2022.lrec-1)
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| Challenge: | Knowledge Graph (KG) Question Answering (QA) is a rapidly growing field in research and industry. |
| Approach: | They propose to create a new leaderboard for any KGQA benchmark dataset as a focal point for the community. |
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