Semantic Matching Techniques for Intelligent Customer Service at Ctrip

Background – With AI increasingly applied across industries, Ctrip has built an intelligent customer service platform that leverages millions of dialogue records and deep‑learning algorithms to provide self‑service for travelers and assist human agents.

Problem Analysis – The core challenge is semantic matching between user queries (UQ) and standard FAQ entries (SQ) in a large knowledge base, requiring high‑precision ranking models. Traditional methods (BM25, bag‑of‑words, PLSA/LDA) are limited, prompting the adoption of word‑embedding models (Word2vec, GloVe) and neural architectures.

Semantic Matching Technology Application – Two main frameworks are used: classification (softmax) and ranking (point‑wise, pair‑wise, list‑wise). Neural models such as DSSM, Siamese LSTM, MatchPyramid, and ESIM are employed, with attention mechanisms (Self‑Attention, Transformer) enabling richer contextual representations. Pre‑trained language models (BERT, GPT, XLNet) further boost performance.

Multi‑Stage Semantic Matching – The system follows a multi‑stage pipeline: a pairwise ranking stage retrieves candidate SQs, followed by a click‑through re‑ranking stage that refines results using user feedback. The ranking loss maximizes the distance between positive (UQ, SQ⁺) and negative (UQ, SQ⁻) pairs, typically using cosine similarity. BERT is integrated as the encoder to improve recall accuracy.

Click‑Through Re‑Ranking – To distinguish semantically similar candidates, an ESIM model with dual LSTM encoders and an attention layer computes fine‑grained similarity scores, leveraging online click data for training.

Multilingual Transfer Learning – Ctrip supports 19 languages; multilingual pre‑training (mBERT, XLM, T5) shares parameters across languages, enabling transfer from high‑resource languages (e.g., Chinese, English) to low‑resource ones (e.g., Japanese, Thai). Experiments show >60% accuracy without fine‑tuning and further gains after limited target‑language data.

KBQA (Knowledge‑Base Question Answering) – For queries requiring reasoning beyond pure matching, the system combines NLU intent detection, slot extraction, and a KBQA engine. Intent recognition uses an Induction Network with few‑shot learning, while slot extraction employs an ALBERT+BiLSTM+CRF pipeline to identify entities such as POIs.

Conclusion – The paper outlines the end‑to‑end semantic matching workflow, from statistical methods to modern deep‑learning models, and demonstrates practical deployments in multilingual settings and KBQA, paving the way for future multimodal, data‑driven intelligent customer service solutions.