{"total":53,"rows":[{"id":"2102.07033","title":"PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them","created":"2021-02-13","cited_by_count":200,"area":null},{"id":"2104.08678","title":"Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation","created":"2021-04-18","cited_by_count":79,"area":null},{"id":"2101.00438","title":"Few-Shot Question Answering by Pretraining Span Selection","created":"2021-01-02","cited_by_count":62,"area":null},{"id":"2101.00133","title":"NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned","created":"2021-01-01","cited_by_count":52,"area":null},{"id":"2210.05528","title":"Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systems","created":"2022-10-11","cited_by_count":44,"area":null},{"id":"2109.03438","title":"ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collections","created":"2021-09-08","cited_by_count":30,"area":null},{"id":"2210.03992","title":"Generative Language Models for Paragraph-Level Question Generation","created":"2022-10-08","cited_by_count":28,"area":null},{"id":"2109.01951","title":"FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models","created":"2021-09-04","cited_by_count":26,"area":null},{"id":"2310.10942","title":"UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large Models","created":"2023-10-17","cited_by_count":25,"area":null},{"id":"2103.11263","title":"Self-Supervised Test-Time Learning for Reading Comprehension","created":"2021-03-20","cited_by_count":22,"area":null},{"id":"2109.07954","title":"Improving Unsupervised Question Answering via Summarization-Informed Question Generation","created":"2021-09-16","cited_by_count":21,"area":null},{"id":"2205.04040","title":"ProQA: Structural Prompt-based Pre-training for Unified Question Answering","created":"2022-05-09","cited_by_count":21,"area":null},{"id":"2102.01065","title":"Do Question Answering Modeling Improvements Hold Across Benchmarks?","created":"2021-02-01","cited_by_count":20,"area":null},{"id":"2109.03685","title":"Open Aspect Target Sentiment Classification with Natural Language Prompts","created":"2021-09-08","cited_by_count":20,"area":null},{"id":"2201.07745","title":"Improving Biomedical Information Retrieval with Neural Retrievers","created":"2022-01-19","cited_by_count":20,"area":null},{"id":"2106.08190","title":"Question Answering Infused Pre-training of General-Purpose Contextualized Representations","created":"2021-06-15","cited_by_count":19,"area":null},{"id":"2205.12609","title":"Generating Information-Seeking Conversations from Unlabeled Documents","created":"2022-05-25","cited_by_count":17,"area":null},{"id":"2305.17002","title":"An Empirical Comparison of LM-based Question and Answer Generation Methods","created":"2023-05-26","cited_by_count":15,"area":null},{"id":"2104.01099","title":"TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training","created":"2021-04-02","cited_by_count":14,"area":null},{"id":"2201.00136","title":"Zero-shot Commonsense Question Answering with Cloze Translation and Consistency Optimization","created":"2022-01-01","cited_by_count":13,"area":null},{"id":"2203.08926","title":"Synthetic Question Value Estimation for Domain Adaptation of Question Answering","created":"2022-03-16","cited_by_count":12,"area":null},{"id":"2310.05007","title":"MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering","created":"2023-10-08","cited_by_count":12,"area":null},{"id":"2211.12707","title":"Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?","created":"2022-11-23","cited_by_count":10,"area":null},{"id":"2305.17416","title":"A Practical Toolkit for Multilingual Question and Answer Generation","created":"2023-05-27","cited_by_count":10,"area":null},{"id":"2109.10346","title":"Relation-Guided Pre-Training for Open-Domain Question Answering","created":"2021-09-21","cited_by_count":7,"area":null},{"id":"2110.08438","title":"Unsupervised Natural Language Inference Using PHL Triplet Generation","created":"2021-10-16","cited_by_count":6,"area":null},{"id":"2210.06781","title":"Closed-book Question Generation via Contrastive Learning","created":"2022-10-13","cited_by_count":6,"area":null},{"id":"2306.04101","title":"Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentation","created":"2023-06-07","cited_by_count":5,"area":null},{"id":"2308.15645","title":"AskIt: Unified Programming Interface for Programming with Large Language Models","created":"2023-08-29","cited_by_count":5,"area":null},{"id":"2203.08928","title":"C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References","created":"2022-03-16","cited_by_count":4,"area":null},{"id":"2208.10813","title":"Unsupervised Question Answering via Answer Diversifying","created":"2022-08-23","cited_by_count":4,"area":null},{"id":"2210.13778","title":"IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension","created":"2022-10-25","cited_by_count":4,"area":null},{"id":"2407.20207","title":"QAEA-DR: A Unified Text Augmentation Framework for Dense Retrieval","created":"2024-07-29","cited_by_count":4,"area":null},{"id":"2107.08582","title":"Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision","created":"2021-07-19","cited_by_count":3,"area":null},{"id":"2211.12641","title":"Leveraging Data Recasting to Enhance Tabular Reasoning","created":"2022-11-23","cited_by_count":3,"area":null},{"id":"2305.02235","title":"AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking","created":"2023-05-03","cited_by_count":3,"area":null},{"id":"2402.16508","title":"Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervision","created":"2024-02-26","cited_by_count":3,"area":null},{"id":"2105.12392","title":"Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction","created":"2021-05-26","cited_by_count":2,"area":null},{"id":"2212.04755","title":"From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine Reader","created":"2022-12-09","cited_by_count":2,"area":null},{"id":"2212.08196","title":"Saved You A Click: Automatically Answering Clickbait Titles","created":"2022-12-15","cited_by_count":2,"area":null},{"id":"2301.01064","title":"PIE-QG: Paraphrased Information Extraction for Unsupervised Question Generation from Small Corpora","created":"2023-01-03","cited_by_count":2,"area":null},{"id":"2204.09593","title":"COOL, a Context Outlooker, and its Application to Question Answering and other Natural Language Processing Tasks","created":"2022-04-01","cited_by_count":1,"area":null},{"id":"2204.12263","title":"Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking","created":"2022-04-26","cited_by_count":1,"area":null},{"id":"2209.11615","title":"Robust Domain Adaptation for Machine Reading Comprehension","created":"2022-09-23","cited_by_count":1,"area":null},{"id":"2107.00315","title":"Interviewer-Candidate Role Play: Towards Developing Real-World NLP Systems","created":"2021-07-01","cited_by_count":0,"area":null},{"id":"2108.13817","title":"Unsupervised Open-Domain Question Answering","created":"2021-08-31","cited_by_count":0,"area":null},{"id":"2304.06623","title":"Exploring the State of the Art in Legal QA Systems","created":"2023-04-13","cited_by_count":0,"area":null},{"id":"2305.16809","title":"GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children","created":"2023-05-26","cited_by_count":0,"area":null},{"id":"2306.04187","title":"Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals","created":"2023-06-07","cited_by_count":0,"area":null},{"id":"2310.13106","title":"Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model","created":"2023-10-19","cited_by_count":0,"area":null},{"id":"2402.04068","title":"Retrieve to Explain: Evidence-driven Predictions for Explainable Drug Target Identification","created":"2024-02-06","cited_by_count":0,"area":null},{"id":"2402.17333","title":"Unsupervised multiple choices question answering via universal corpus","created":"2024-02-27","cited_by_count":0,"area":null},{"id":"2602.22182","title":"LiCQA : A Lightweight Complex Question Answering System","created":"2026-02-25","cited_by_count":0,"area":null}],"next_cursor":null}