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.
Meituan’s 2021 ACL contributions comprise seven accepted papers—six long and one short—introducing novel approaches to event argument decoding, cross‑domain slot transfer, contrastive out‑of‑domain detection, novel slot discovery, self‑supervised sentence representation, unsupervised semantic parsing, and pseudo‑query‑enhanced dense retrieval, inviting further research and collaboration.
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.
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.
NL2SQL converts natural language questions into executable SQL, bridging the gap between users and databases; the article reviews its value, historical roots, academic positioning, major datasets, current models, challenges, and future directions, highlighting its potential to reshape data interaction across industries.
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.
