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 AviationDataset

0 likes · 5 min read

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

JD Cloud Developers

Mar 11, 2022 · Artificial Intelligence

How JD’s NR‑Rino Model Cracked the DROP Benchmark with 90% Accuracy

The JD Intelligent Customer Service team’s NR‑Rino model topped the DROP leaderboard at 90.26% accuracy by enhancing multi‑head predictor architecture and training strategies, showcasing advanced discrete reasoning for machine reading comprehension and promising broader AI applications in finance, logistics, and health.

AIDROPNR-Rino

0 likes · 9 min read

How JD’s NR‑Rino Model Cracked the DROP Benchmark with 90% Accuracy

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

0 likes · 18 min read

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.

DROPKnowledge GraphNLP

0 likes · 7 min read

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

Alibaba Cloud Developer

Jul 3, 2019 · Artificial Intelligence

Alibaba AI Sets New Record in MS MARCO Reading Comprehension, Surpassing Humans

Alibaba's AI model topped the MS MARCO reading comprehension challenge, achieving the highest scores in document ranking and open‑domain question answering, even surpassing human performance, thanks to its deep‑cascade BERT‑based architecture that mimics human reading and is already deployed in e‑commerce applications.

BERTMS MARCOReading Comprehension

0 likes · 5 min read

Alibaba AI Sets New Record in MS MARCO Reading Comprehension, Surpassing Humans