{"total":19,"rows":[{"id":"2110.11624","title":"SciCap: Generating Captions for Scientific Figures","created":"2021-10-22","cited_by_count":77,"area":null},{"id":"2109.09138","title":"Multi-Task Learning in Natural Language Processing: An Overview","created":"2021-09-19","cited_by_count":62,"area":null},{"id":"2108.00648","title":"From LSAT: The Progress and Challenges of Complex Reasoning","created":"2021-08-02","cited_by_count":43,"area":null},{"id":"2101.11429","title":"TSQA: Tabular Scenario Based Question Answering","created":"2021-01-14","cited_by_count":21,"area":null},{"id":"2105.14454","title":"NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation","created":"2021-05-30","cited_by_count":16,"area":null},{"id":"2102.05474","title":"Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge","created":"2021-02-10","cited_by_count":10,"area":null},{"id":"2303.18162","title":"ViMMRC 2.0 -- Enhancing Machine Reading Comprehension on Vietnamese Literature Text","created":"2023-03-31","cited_by_count":5,"area":null},{"id":"2108.13875","title":"When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions","created":"2021-08-31","cited_by_count":3,"area":null},{"id":"2109.08820","title":"Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems","created":"2021-09-18","cited_by_count":3,"area":null},{"id":"2203.10249","title":"Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension","created":"2022-03-19","cited_by_count":3,"area":null},{"id":"2107.00368","title":"Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems","created":"2021-07-01","cited_by_count":2,"area":null},{"id":"2109.12996","title":"Context-guided Triple Matching for Multiple Choice Question Answering","created":"2021-09-27","cited_by_count":2,"area":null},{"id":"2103.16102","title":"XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract Meaning","created":"2021-03-30","cited_by_count":1,"area":null},{"id":"2203.14103","title":"Lite Unified Modeling for Discriminative Reading Comprehension","created":"2022-03-26","cited_by_count":1,"area":null},{"id":"2310.18070","title":"Multi-grained Evidence Inference for Multi-choice Reading Comprehension","created":"2023-10-27","cited_by_count":1,"area":null},{"id":"2105.12051","title":"NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension","created":"2021-05-25","cited_by_count":0,"area":null},{"id":"2402.11177","title":"A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction","created":"2024-02-17","cited_by_count":0,"area":null},{"id":"2404.17949","title":"Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering","created":"2024-04-27","cited_by_count":0,"area":null},{"id":"2503.18062","title":"Investigating Recent Large Language Models for Vietnamese Machine Reading Comprehension","created":"2025-03-23","cited_by_count":0,"area":null}],"next_cursor":null}