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Machine Reading Comprehension
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Machine Reading Comprehension is one of the key problems in Natural Language Understanding, where the task is to read and comprehend a given text passage, and then answer questions based on it.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Understanding LLMs' Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From
Investigating Recent Large Language Models for Vietnamese Machine Reading Comprehension
MRCEval: A Comprehensive, Challenging and Accessible Machine Reading Comprehension Benchmark
Pay Attention to Real World Perturbations! Natural Robustness Evaluation in Machine Reading Comprehension
RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following
Visualizing attention zones in machine reading comprehension models
RoBIn: A Transformer-Based Model For Risk Of Bias Inference With Machine Reading Comprehension
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models
Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning
SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models
Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction
Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets
Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity Recognition
Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation
Comparison of Open-Source and Proprietary LLMs for Machine Reading Comprehension: A Practical Analysis for Industrial Applications
2DP-2MRC: 2-Dimensional Pointer-based Machine Reading Comprehension Method for Multimodal Moment Retrieval
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension
QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering
Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension
From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering
emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information
Interpreting Themes from Educational Stories
The Death of Feature Engineering? BERT with Linguistic Features on SQuAD 2.0
ArabicaQA: A Comprehensive Dataset for Arabic Question Answering
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pages
WangchanLion and WangchanX MRC Eval
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training
QASE Enhanced PLMs: Improved Control in Text Generation for MRC
VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension
Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition
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