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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 OPPO's large‑scale knowledge graph, detailing the algorithmic challenges and solutions for entity classification, alignment, information extraction, and query parsing, and explains how these techniques power the XiaoBu assistant's knowledge‑based QA, search, and recommendation services while outlining future research directions.
This article explains how voice assistants rely on NLP's three processing layers, examines the shortcomings of the traditional DIS semantic structure, introduces the hierarchical CMRL schema with its six element types, and presents two neural models—copy‑write seq2seq and seq2tree—for accurate semantic parsing of complex commands.
This article explains how voice assistants rely on a three‑layer NLP pipeline (lexical, syntactic, and semantic analysis), discusses the shortcomings of the traditional DIS (Domain‑Intent‑Slot) structure for complex commands, and introduces the hierarchical CMRL schema along with two neural models (copy‑write seq2seq and seq2tree) for converting natural language into structured logical expressions.
This article reviews the Text-to-SQL semantic parsing task, covering its motivation, dataset landscape, major model architectures such as pointer networks, sequence‑to‑set, and grammar‑based approaches, evaluation metrics, the newly built DuSQL dataset and DuParser system, real‑world deployments, and remaining research challenges.
Query understanding (QU) involves lexical, syntactic, and semantic analysis of user queries to enable effective search recall and ranking, covering modules such as preprocessing, correction, expansion, segmentation, intent detection, term importance, and guidance, with detailed discussion of algorithms, models, and system architecture.
This article reviews recent advances in machine reasoning applied to multi‑turn semantic parsing and conversational question answering, describing how grammar, context, and data knowledge are integrated via sequence‑to‑action models and meta‑learning to achieve state‑of‑the‑art results on the CSQA benchmark.
This article explains how query understanding (QP) transforms user search queries into structured semantic blocks and intent categories using rule‑based NLP, entity recognition, and post‑processing, and describes its taxonomy, implementation details, and practical impact on search engine results.
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.
This report explains how Alibaba uses knowledge graph construction, semantic parsing, and structured answer generation to overcome knowledge management and language understanding challenges in next‑generation intelligent customer service, delivering efficient reuse, precise comprehension, and fine‑grained management of knowledge.
An extensive overview of ACL 2018, featuring acceptance statistics, award-winning papers, tutorial insights, and concise summaries of notable research across machine translation, semantic parsing, question answering, domain adaptation, text classification, summarization, dialogue systems, generation, and related tools.