Papers Machine Reading Comprehension
“Machine Reading Comprehension” 태그가 달린 논문 562편 · 필터 해제
Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction
Advancements in AI and natural language processing have revolutionized machine-human language interactions, with question answering (QA) systems playing a pivotal role. The knowledge base question answering (KBQA) task, …
Information RetrievalKnowledge Base Question AnsweringKnowledge GraphsMachine Reading Comprehension+2Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets
This paper provides a thorough examination of recent developments in the field of multi-choice Machine Reading Comprehension (MRC). Focused on benchmark datasets, methodologies, challenges, and future trajectories, our g…
Few-Shot LearningMachine Reading ComprehensionMulti-Choice MRCMultiple-choice+2Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity Recognition
Grounded Multimodal Named Entity Recognition (GMNER) is an emerging information extraction (IE) task, aiming to simultaneously extract entity spans, types, and corresponding visual regions of entities from given sentence…
Grounded Multimodal Named Entity RecognitionMachine Reading Comprehensionnamed-entity-recognitionNamed Entity Recognition+2Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation
Instruction-tuned Large Language Models (LLMs) have achieved remarkable performance across various benchmark tasks. While providing instructions to LLMs for guiding their generations is user-friendly, assessing their ins…
Instruction FollowingMachine Reading ComprehensionReading ComprehensionComparison of Open-Source and Proprietary LLMs for Machine Reading Comprehension: A Practical Analysis for Industrial Applications
Large Language Models (LLMs) have recently demonstrated remarkable performance in various Natural Language Processing (NLP) applications, such as sentiment analysis, content generation, and personalized recommendations. …
BenchmarkingMachine Reading ComprehensionQuestion AnsweringReading Comprehension+12DP-2MRC: 2-Dimensional Pointer-based Machine Reading Comprehension Method for Multimodal Moment Retrieval
Moment retrieval aims to locate the most relevant moment in an untrimmed video based on a given natural language query. Existing solutions can be roughly categorized into moment-based and clip-based methods. The former o…
Boundary DetectionMachine Reading ComprehensionMoment RetrievalReading Comprehension+1Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training
Large language models (LLMs) aligned through reinforcement learning from human feedback (RLHF) have quickly become one of the dominant paradigms for building intelligent conversational assistant agents. However, despite …
Machine Reading ComprehensionQuestion AnsweringReading ComprehensionText to SQL+1Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension
Large language models (LLMs) have shown remarkable performance on many tasks in different domains. However, their performance in closed-book biomedical machine reading comprehension (MRC) has not been evaluated in depth.…
Machine Reading ComprehensionRAGReading ComprehensionRetrieval+2QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering
Extractive Question Answering (EQA) in Machine Reading Comprehension (MRC) often faces the challenge of dealing with semantically identical but format-variant inputs. Our work introduces a novel approach, called the ``Qu…
Extractive Question-AnsweringMachine Reading ComprehensionQuestion AnsweringReading ComprehensionEnhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension
Machine Reading Comprehension (MRC) poses a significant challenge in the field of Natural Language Processing (NLP). While mainstream MRC methods predominantly leverage extractive strategies using encoder-only models suc…
Machine Reading ComprehensionReading ComprehensionTransfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering
Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct answer from a set of options based on a given passage and question. The existing methods employ the pre-trained language model as the encoder, …
Binary ClassificationLanguage ModelingLanguage ModellingMachine Reading Comprehension+4From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
The rapid evolution of Natural Language Processing (NLP) has favoured major languages such as English, leaving a significant gap for many others due to limited resources. This is especially evident in the context of data…
BelebeleExtractive Question-AnsweringMachine Reading ComprehensionMultiple-choice+3PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering
Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD), particularly those dominated by lengthy textual content like research journal articles. Existing studies primarily foc…
ArticlesInformation RetrievalMachine Reading ComprehensionQuestion Answering+4emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information
Machine Reading Comprehension (MRC) holds a pivotal role in shaping Medical Question Answering Systems (QAS) and transforming the landscape of accessing and applying medical information. However, the inherent challenges …
Decision MakingMachine Reading ComprehensionMedical Question AnsweringQuestion Answering+1Interpreting Themes from Educational Stories
Reading comprehension continues to be a crucial research focus in the NLP community. Recent advances in Machine Reading Comprehension (MRC) have mostly centered on literal comprehension, referring to the surface-level un…
Machine Reading ComprehensionReading ComprehensionThe Death of Feature Engineering? BERT with Linguistic Features on SQuAD 2.0
Machine reading comprehension is an essential natural language processing task, which takes into a pair of context and query and predicts the corresponding answer to query. In this project, we developed an end-to-end que…
Feature EngineeringMachine Reading ComprehensionQuestion AnsweringReading ComprehensionChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pages
Question answering (QA) and Machine Reading Comprehension (MRC) tasks have significantly advanced in recent years due to the rapid development of deep learning techniques and, more recently, large language models. At the…
Machine Reading ComprehensionOptical Character Recognition (OCR)Question AnsweringReading ComprehensionArabicaQA: A Comprehensive Dataset for Arabic Question Answering
In this paper, we address the significant gap in Arabic natural language processing (NLP) resources by introducing ArabicaQA, the first large-scale dataset for machine reading comprehension and open-domain question answe…
BenchmarkingMachine Reading ComprehensionOpen-Domain Question AnsweringPassage Retrieval+4WangchanLion and WangchanX MRC Eval
This technical report describes the development of WangchanLion, an instruction fine-tuned model focusing on Machine Reading Comprehension (MRC) in the Thai language. Our model is based on SEA-LION and a collection of in…
Instruction FollowingMachine Reading ComprehensionReading ComprehensionMRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training
In medical information extraction, medical Named Entity Recognition (NER) is indispensable, playing a crucial role in developing medical knowledge graphs, enhancing medical question-answering systems, and analyzing elect…
Knowledge GraphsMachine Reading ComprehensionMedical Named Entity RecognitionMedical Question Answering+6