Reading Comprehension

DataFunTalk

Jul 8, 2022 · Artificial Intelligence

Civil Aviation QA Competition (CCL2022‑DQAB): Task Description, Data, Evaluation Metrics, and Prizes

The CCL2022‑DQAB competition, organized by Beihang University and AVIC Mobile Technology, invites participants to develop reading‑comprehension models for extracting accurate question‑answer pairs from civil aviation texts, offering detailed task definitions, evaluation criteria, dataset statistics, a prize structure, and a competition schedule.

AICivil AviationNLP

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Civil Aviation QA Competition (CCL2022‑DQAB): Task Description, Data, Evaluation Metrics, and Prizes

DataFunTalk

Nov 12, 2021 · Artificial Intelligence

Xiaomi Xiao AI Intelligent Question‑Answering System: Architecture, Techniques, and Applications

This article presents a comprehensive overview of Xiaomi's Xiao AI intelligent QA system, detailing its background, three core answering modules—knowledge‑graph QA, retrieval‑based FAQ, and reading‑comprehension—and the underlying methods such as template matching, cross‑domain semantic parsing, path‑based reasoning, semantic retrieval, and neural matching, while also discussing performance results and practical trade‑offs.

AINLPReading Comprehension

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Xiaomi Xiao AI Intelligent Question‑Answering System: Architecture, Techniques, and Applications

AntTech

Sep 27, 2020 · Artificial Intelligence

Question Directed Graph Attention Network for Numerical Reasoning over Text (QDGAT)

The paper introduces QDGAT, a question‑directed graph attention network that enhances numerical reasoning in reading comprehension by explicitly modeling relationships among numbers, entities, and questions, and demonstrates its effectiveness through extensive experiments on the DROP dataset.

DROPNLPQDGAT

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Question Directed Graph Attention Network for Numerical Reasoning over Text (QDGAT)