A Multi-Domain Framework for Textual Similarity. A Case Study on Question-to-Question and Question-Answering Similarity Tasks (L18-1)
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| Challenge: | Community Question Answering websites are becoming popular and useful source of information for users. |
| Approach: | They propose to use community question answering forum to detect similar questions . they use question-answering similarity task to provide correct answers . |
| Outcome: | The proposed framework provides the first framework on the evaluation of similar questions and question-answering detection on a multi-domain corpora. |
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| Challenge: | Existing work on multi-domain, multi-lingual question answering is limited to the same language. |
| Approach: | They curate 500 articles in six different domains from the web and create question-answer pairs . they develop a deep learning based model for classifying an input question into coarse and finer categories . |
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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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Open-Domain Question Answering (2020.acl-tutorials)
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| Challenge: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering (QA) |
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Adversarial Domain Adaptation for Duplicate Question Detection (D18-1)
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| Challenge: | Recent years have seen the rise of community question answering forums . duplicate questions easily become ubiquitous as users often ask the same question, possibly in a slightly different formulation, making it difficult to find the best (or one correct) answer. |
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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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Multi-Domain Multilingual Question Answering (2021.emnlp-tutorials)
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| Challenge: | Question answering (QA) is one of the most challenging tasks in natural language processing. |
| Approach: | a tutorial examines the state-of-the-art approaches to multi-domain and multilingual QA . they introduce standard benchmarks and discuss out-of the-box training with open-domain QA systems . |
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| Challenge: | Community-based question answering (CQA) has become an essential component of online services. |
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How to Compare Things Properly? A Study of Argument Relevance in Comparative Question Answering (2025.acl-long)
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Irina Nikishina, Saba Anwar, Nikolay Dolgov, Maria Manina, Daria Ignatenko, Artem Shelmanov, Chris Biemann
| Challenge: | Comparative Question Answering (CQA) is a task that involves processing information and diverse viewpoints. |
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Technical Question Answering across Tasks and Domains (2021.naacl-industry)
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| Challenge: | Existing methods for technical QA have a limited data size and question and answer overlaps . |
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