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Machine Reading Comprehension

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.

Source: Making Neural Machine Reading Comprehension Faster

Papers

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Showing 501–550 of 555 papers

TitleStatusHype
G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents—0
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions—0
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need—0
GenNet : Reading Comprehension with Multiple Choice Questions using Generation and Selection model—0
Graph-Based Knowledge Integration for Question Answering over Dialogue—0
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model—0
Graph Sequential Network for Reasoning over Sequences—0
Have You Seen That Number? Investigating Extrapolation in Question Answering Models—0
ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension—0
Hierarchical Evaluation Framework: Best Practices for Human Evaluation—0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method—0
Explicit Contextual Semantics for Text Comprehension—0
Improved Synthetic Training for Reading Comprehension—0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding—0
Improving Cross-Lingual Reading Comprehension with Self-Training—0
Improving Machine Reading Comprehension via Adversarial Training—0
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge—0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite—0
Improving Pre-Trained Multilingual Models with Vocabulary Expansion—0
Improving Pre-Trained Multilingual Model with Vocabulary Expansion—0
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior—0
Improving Zero-Shot Event Extraction via Sentence Simplification—0
Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension—0
Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning—0
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension—0
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference—0
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups—0
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension—0
Integrated Triaging for Fast Reading Comprehension—0
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model—0
Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension—0
Interpretable Semantic Role Relation Table for Supporting Facts Recognition of Reading Comprehension—0
Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation—0
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension—0
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension—0
Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension—0
Investigating Recent Large Language Models for Vietnamese Machine Reading Comprehension—0
Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task—0
基於BERT模型之多國語言機器閱讀理解研究(Multilingual Machine Reading Comprehension based on BERT Model)—0
基于话头话体共享结构信息的机器阅读理解研究(Rearch on Machine reading comprehension based on shared structure information between Naming and Telling)—0
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)—0
基于小句复合体的中文机器阅读理解研究(Machine Reading Comprehension Based on Clause Complex)—0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering—0
Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation—0
KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning—0
KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information—0
Knowledge Based Machine Reading Comprehension—0
Know your tools well: Better and faster QA with synthetic examples—0
KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension—0
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