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The ICDAR 2023 BDVT-QA competition, organized by Alibaba DAMO Academy, introduces a novel dataset of 1,000 born‑digital video clips for end‑to‑end video text recognition and video text question answering, offering cash prizes, detailed dataset access, and a lineup of leading academic and industry experts.
This presentation details the XiaoAi intelligent assistant’s QA system, covering its application scenarios, information extraction techniques (including relation and event extraction with SPO/PSO models), graph‑based question answering methods, cross‑domain slot extraction, path retrieval, and practical Q&A insights.
This article presents an in‑depth overview of intelligent question answering in QQ Browser search, covering its background, the core KBQA and DeepQA technologies, system architecture, challenges, recent advances such as end‑to‑end, knowledge‑guided and multimodal QA, and practical Q&A for deployment.
This article presents a comprehensive overview of temporal knowledge graphs, outlines the challenges of building question‑answering systems over them, introduces the TSQA method with its three‑step pipeline for time‑sensitive reasoning, and reports experimental results showing significant improvements on complex queries.
This article introduces the intelligent question‑answering technology used in WeChat Search, covering background, knowledge‑graph‑based QA, document‑based QA, technical pipelines, key modules such as entity linking and relation recognition, and future research directions.
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.
This article presents the architecture, techniques, and practical solutions behind intelligent question answering in QQ Browser's search engine, covering knowledge‑graph based QA (KBQA), machine‑reading‑comprehension QA (DeepQA), and information‑retrieval QA (IRQA), and discusses system design, model optimization, and future directions.
This article details the design, iterative development, and performance optimizations of 58's AI‑driven intelligent Q&A chatbot, covering its overall three‑layer architecture, the QABot, TaskBot, and answer‑recommendation modules, as well as dynamic strategy adjustment, caching mechanisms, and real‑world deployment results.
The qa_match open‑source tool from 58 Tongcheng, now at version 1.3, introduces semi‑automatic knowledge‑base mining for cold‑start and online scenarios and upgrades its Simple Pre‑trained Model (SPTM) with Transformer‑based feature representation to improve question‑answer matching performance.
This article introduces the typical applications of intelligent question answering, compares chat‑type, knowledge‑type and task‑type bots, and then details the end‑to‑end architecture, knowledge‑base construction, semantic‑equivalence modeling with BERT‑BIMPM, knowledge‑graph QA pipelines, and NL2SQL techniques, concluding with practical deployment insights.
This article describes Meituan's intelligent question answering system, detailing its three core capabilities—Document QA, Community QA, and Knowledge‑Graph QA—along with the underlying machine‑reading comprehension models, multi‑task learning, answer ranking, and real‑world deployment scenarios across travel, hotel, and retail services.
The article reviews the open‑source qa_match tool from 58.com, detailing its deep‑learning based question‑answer matching architecture, hierarchical knowledge‑base support, lightweight pre‑training model SPTM, and practical applications, while summarizing the live‑stream presentation and Q&A session.
The article introduces qa_match V1.1, an open‑source, Apache‑licensed lightweight question‑answer matching system that adds a simple pre‑trained language model (SPTM), supports one‑level knowledge bases, details model architecture, training resources, performance benchmarks, future plans, and contribution guidelines.
The article presents qa_match, a TensorFlow‑based open‑source QA matching system that combines BiLSTM‑attention domain classification with DSSM intent matching, explains its architecture, training workflow, features, future roadmap, and how developers can contribute to the project.
This article reviews the evolution of semantic matching models for travel question‑answering, covering traditional keyword and probabilistic methods, deep‑learning encoders such as LSTM, CNN, and Transformer, interaction‑based architectures like MatchPyramid and hCNN, as well as transfer‑learning and multilingual extensions to improve practical deployment.
This article describes the design, model selection, experimental evaluation, and engineering optimization of an AI‑driven post‑sale question‑answering system for hotel services, covering FAQ construction, intent detection, deep‑learning matching models such as DSSM, ESIM, BERT, and their performance and latency trade‑offs.
The article introduces qa_match, an open‑source lightweight QA matching tool built on TensorFlow that combines BiLSTM‑based domain classification, DSSM‑based intent matching, and a model‑fusion strategy to deliver accurate, multi‑type responses for intelligent customer service applications.
This article presents an in‑depth overview of Alibaba's Xiaomì conversational AI system, describing how structured knowledge—including FAQs, phrase‑based knowledge, knowledge graphs, and machine‑read documents—is organized into a two‑level schema and applied to knowledge‑based QA (KBQA) and event‑based QA (EBQA) with detailed model pipelines, ranking, type inference, and recommendation techniques, while also discussing practical challenges and future directions.
This talk presents how Meituan leverages knowledge‑graph QA (KBQA) across restricted and complex smart‑interaction scenarios, compares semantic‑parsing and information‑retrieval approaches, introduces three‑layer concept nodes to handle entity explosion and non‑connected queries, and outlines architectural refinements for multi‑turn dialogue integration.
At SIGIR 2019 in Paris, Alipay presented two AI research papers—one applying reinforcement learning to predict user intent in customer‑service bots and another introducing the unsupervised QUEST method that builds noisy quasi‑knowledge graphs for answering complex multi‑document questions.