Intelligent Question Answering: Scenarios, Architecture, and Technical Implementations (QA, Knowledge‑Graph QA, NL2SQL)

Intelligent dialogue systems have attracted increasing attention due to their commercial value, covering chat, knowledge, task, and reading‑comprehension types, and are widely used in smart customer service, speakers, and in‑vehicle assistants.

Typical scenarios include open‑domain chit‑chat, task‑driven multi‑turn dialogs (e.g., voice assistants), and information‑type QA such as factual queries.

Product architecture consists of speech recognition, ASR correction, intent detection (including emotion, business intent, dialog management, exception handling), response generation, and auxiliary modules for user profiling and analysis.

The system includes a knowledge base, dialog models, configuration center, multi‑channel access, and backend management. Different bots (QA Bot, KG Bot, DB Bot, Task Bot) use specialized knowledge bases.

Semantic equivalence modeling is crucial for matching user questions with stored QA pairs. Traditional similarity measures (cosine, edit distance, BM25) are insufficient; a BERT‑based BIMPM model replaces word2vec embeddings with BERT outputs and substitutes Bi‑LSTM with Transformers, achieving state‑of‑the‑art results on Quora and SLNI datasets.

Knowledge‑graph QA converts user queries into SPARQL statements. The pipeline includes entity recognition (with alias dictionary and Elasticsearch), question classification (chain vs. sandwich, hop count, entity vs. relation), slot filling, entity linking (using MatchZoo and stacking models), and path ranking via a Siamese network.

NL2SQL translates natural language into SQL queries. It involves tokenization, POS tagging, entity recognition, dependency parsing, and slot filling (select fields, aggregations, filters). Classic Seq2Seq X‑SQL models use MT‑DNN encodings and attention mechanisms, while a newer approach combines dependency trees with X‑SQL to improve accuracy.

Experimental results show the knowledge‑graph QA model achieved an F1 of 0.901 in the 2020 CCKS competition with 200 ms latency on GPU, and the NL2SQL pipeline reaches over 90 % parsing accuracy.

Conclusion : Pre‑trained deep transfer learning models will become standard in intelligent QA; NL2SQL remains a hot research frontier; vertical industry QA scenarios are easier to deploy and provide better user experience.