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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 11–20 of 555 papers

TitleStatusHype
Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension—0
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning—0
SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models—0
Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset ConstructionCode0
Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets—0
Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity RecognitionCode1
Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation—0
Comparison of Open-Source and Proprietary LLMs for Machine Reading Comprehension: A Practical Analysis for Industrial Applications—0
2DP-2MRC: 2-Dimensional Pointer-based Machine Reading Comprehension Method for Multimodal Moment Retrieval—0
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training—0
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