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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 401–425 of 555 papers

TitleStatusHype
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
Label Dependent Deep Variational Paraphrase Generation—0
Learning to Ask Unanswerable Questions for Machine Reading Comprehension—0
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training—0
Learning to Generate Questions by Recovering Answer-containing Sentences—0
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension—0
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification—0
Listening Comprehension over Argumentative Content—0
Logic Pre-Training of Language Models—0
Low-Resource Generation of Multi-hop Reasoning Questions—0
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension—0
Machine Reading Comprehension: a Literature Review—0
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction—0
Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots—0
Machine Reading Comprehension: Generative or Extractive Reader?—0
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network—0
Machine Reading Comprehension using Case-based Reasoning—0
Machine Reading Comprehension with Enhanced Linguistic Verifiers—0
Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition—0
Making Neural Machine Reading Comprehension Faster—0
MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension—0
MC\^2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension—0
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