NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets (2020.emnlp-demos)
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| Challenge: | Existing tools for Question Answering (QA) have challenges that limit their use in practice. |
| Approach: | They propose a library that integrates with existing infrastructure and offers helpful defaults for QA subtasks. |
| Outcome: | NeuralQA integrates well with existing infrastructure and offers helpful defaults for QA subtasks. |
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| Challenge: | Community Question Answering is a research area that benefits from deep linguistic analysis . previous cQA challenges have shown that neural approaches are not enough to deliver state-of-the-art results . |
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PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development (2023.acl-demo)
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Avi Sil, Jaydeep Sen, Bhavani Iyer, Martin Franz, Kshitij Fadnis, Mihaela Bornea, Sara Rosenthal, Scott McCarley, Rong Zhang, Vishwajeet Kumar, Yulong Li, Md Arafat Sultan, Riyaz Bhat, Juergen Bross, Radu Florian, Salim Roukos
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ReQA: An Evaluation for End-to-End Answer Retrieval Models (D19-58)
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| Challenge: | Open-domain question answering aims to answer questions through text retrieval and reading comprehension . but, the success of these models relies on a massive volume of training data, which is not available in other languages . a new dataset aims at investigating cross-lingual OpenQA . |
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UQA: Corpus for Urdu Question Answering (2024.lrec-main)
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| Challenge: | Urdu is a low-resource language with over 70 million native speakers . expanding the reach of NLP to languages other than English is crucial for advancing multilingual AI systems. |
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MESAQA: A Dataset for Multi-Span Contextual and Evidence-Grounded Question Answering (2025.coling-main)
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| Challenge: | Existing question answering systems focus on extracting answers from single spans, but real-world scenarios require synthesizing information from multiple spans. |
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Towards Interpreting BERT for Reading Comprehension Based QA (2020.emnlp-main)
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| Challenge: | Pretrained language models such as ELMO and XLNet have achieved state-of-the-art performance on various NLP tasks. |
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ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers (2022.acl-long)
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| Challenge: | Existing datasets for reading comprehension have deterministic answers, but questions in the real world do not always have definite answers. |
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| Challenge: | Existing KBQA methods focus on the natural language but ignore textual information carried by the nodes and edges. |
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CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training (2022.findings-naacl)
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| Challenge: | Existing approaches to answer open domain questions rely on unlabeled text or synthetically generated question-answer pairs. |
| Approach: | They propose a large-scale open-domain question-answering dataset based on the Common Crawl project that can be used to in-domain pre-train popular language models. |
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