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. |
| Outcome: | The proposed dataset achieves promising results in zero-shot, low resource and fine-tuned settings across multiple tasks, models and benchmarks. |
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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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PerCQA: Persian Community Question Answering Dataset (2022.lrec-1)
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| Challenge: | Community Question Answering (CQA) forums provide answers to many real-life questions. |
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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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Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervision (2024.emnlp-main)
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| Challenge: | Cross-lingual open domain question answering requires multiple models, requiring substantial annotated datasets and auxiliary resources to bridge between languages. |
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MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2021.tacl-1)
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| Challenge: | Existing multilingual QA datasets lack linguistic diversity and comparable evaluation between languages. |
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CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data (2020.lrec-1)
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Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, Edouard Grave
| Challenge: | Pre-training text representations have led to significant improvements in many areas of natural language processing. |
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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. |
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A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines (P18-4)
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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 . |
| Approach: | They propose a framework to distribute computation of cQA tasks over computer clusters . community question answering is a research area that benefits from deep linguistic analysis . |
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ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search (2024.lrec-main)
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| Challenge: | Existing approaches to code question answering use bi-modal and unimodal pretraining to align text and code representations. |
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Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models (2021.emnlp-main)
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| Challenge: | Existing KBQA methods focus on the natural language but ignore textual information carried by the nodes and edges. |
| Approach: | They propose to perform relation extraction, relation matching, and relation reasoning tasks to align the natural language expressions to the relations in the KB and reason over the missing connections. |
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