Design and Implementation of a Multi‑Turn Conversational Chatbot

Multi‑turn conversational chatbots are a typical application scenario of artificial intelligence and present significant challenges. They require heterogeneous knowledge representation, extraction, reasoning, and the integration of core AI technologies such as natural language understanding.

Current chatbots range from simple keyword‑matching systems to cutting‑edge end‑to‑end deep‑learning generators, serving various use cases such as casual chat, task‑oriented dialogue, and QA.

Designing a Chatbot

Understanding user utterances is the first step. The system converts natural language into a structured representation, then queries a knowledge base (QA pairs, rule sets, business processes) to generate a response.

1. Building a Language Model

A language model transforms free‑form text into a multi‑label structured output. The process includes:

1) Text preprocessing – tokenization, word vectors, word‑sense disambiguation.

2) Sample preparation – annotating classic utterances on the myModel platform.

3) Sequence modeling – a CNN + LSTM hybrid network for multi‑label classification.

Even in a narrow domain, building a robust semantic model is difficult; minor changes in word order or the addition of irrelevant words can drastically affect predictions.

2. Applying the Language Model in the System

The system distinguishes three layers of understanding:

• Topic modeling – classifies the current conversation topic.

• Intent understanding – uses Act‑Slot models to capture intents and entity types.

• Sentiment modeling – classifies emotions such as profanity, anger, neutrality, or praise.

Value extraction can be performed via regular expressions, lexical lookup, or an LSTM model that categorizes entities into ENV, MAPID, ROLE, TEAM, LOGIN, EQUIP, etc.

3. Answer Engine

A business description language based on XML defines three layers:

• Low‑level control units – if, while, sequence, parallel.

• Common components – e.g., request QQ number, game name, emotion soothing.

• High‑level services – e.g., equipment recovery, theft handling.

The workflow control engine manages tasks (Task1‑Task5) and supports topic‑switch listeners that can interrupt and redirect the conversation based on user input.

Example tasks include penalty query, penalty appeal, password change, and real‑name verification, each of which can be triggered by an initial question or nested within a dialogue.

TP XiaoTian Bot

Based on the described design, the TP XiaoTian chatbot is deployed on the Game Security Center public account, providing services such as penalty query, appeal, report, and account freeze.

In practice, a production‑grade chatbot also involves large‑scale data annotation, FAQ answering, rapid configuration, diverse response generation, and casual chat capabilities.