Elvis vs. M. Jackson: Who has More Albums? Classification and Identification of Elements in Comparative Questions (2022.lrec-1)
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| Challenge: | Comparative Question Answering (cQA) is the task of providing accurate answers to questions . most question answering systems focus on answering factoid questions, but they fail at answering comparative questions in an efficient argumentative manner. |
| Approach: | They propose two new open-domain datasets for identifying and labeling comparative questions . they use a binary classification task and an unsupervised sequence labeling task . |
| Outcome: | The proposed datasets reach close-to-human results on a binary classification task with a neural model using ALBERT embeddings. |
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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. |
| Approach: | They construct a dataset of arguments annotated with their relevance and use it to answer comparative questions. |
| Outcome: | The proposed dataset contains arguments annotated with their relevance and enables precise traceability and faithfulness. |
XQA: A Cross-lingual Open-domain Question Answering Dataset (P19-1)
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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 . |
| Approach: | They propose to use a dataset for cross-lingual OpenQA research to test models . they use XQA dataset to train models with large volumes of labeled data . |
| Outcome: | The proposed model achieves best results in almost all target languages while the performance is lower than that of English. |
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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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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ELQA: A Corpus of Metalinguistic Questions and Answers about English (2023.acl-long)
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| Challenge: | ELQA corpus is metalinguistic—it consists of language about language. |
| Approach: | They present a corpus of questions and answers in and about the English language . they use a free-form question answering task and multiple LLMs to analyze their capacity . |
| Outcome: | The ELQA corpus covers grammar, meaning, fluency, and etymology . the results can be used to investigate metalinguistic capabilities of NLU models . |
Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language (2021.eacl-demos)
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Viktoriia Chekalina, Alexander Bondarenko, Chris Biemann, Meriem Beloucif, Varvara Logacheva, Alexander Panchenko
| Challenge: | Comparative QA is a challenging task since it requires collecting evidence from many different sources. |
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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) |
| Approach: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering . focus will shift to cutting- edge models proposed for open- domain QA . |
| Outcome: | The tutorial will cover cutting-edge research in open-domain question answering (QA) it will cover two-stage retriever-reader approaches, dense retriever and end-to-end training, and retriever free methods . |
DebateQA: Evaluating Question Answering on Debatable Knowledge (2026.findings-eacl)
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| Challenge: | Existing QA benchmarks that provide fixed answers to debatable questions are inadequate for evaluating their performance. |
| Approach: | They propose to use a dataset of 2,941 debatable questions to assess their ability to provide comprehensive answers to inherently debatably asked questions. |
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IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions (2023.emnlp-main)
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| Challenge: | Existing open-domain QA tasks focus on questions whose answer can be deduced directly from global factual knowledge. |
| Approach: | They propose a dataset where each question is based on a counterfactual presupposition via an "if" clause. |
| Outcome: | The IfQA dataset contains 3,800 questions that were annotated by crowdworkers on relevant Wikipedia passages. |
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. |