Modeling the Chatbot Quality of Services (CQoS) Using Word Embedding to Intelligently Detect Inappropriate Responses
- Publisher:
- Springer
- Publication Type:
- Conference Proceeding
- Citation:
- Advanced Information Networking and Applications, 2020, 1151, pp. 60-70
- Issue Date:
- 2020-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Almansor-Hussain2020_Chapter_ModelingTheChatbotQualityOfSer.pdf | 1.76 MB |
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The rapid growth of intelligent chatbots as conversational agents with the assistance of artificial intelligence has recently attracted much research attention. The major role of a chatbot is to generate appropriate responses to the user, however sometimes the chatbot fails to understand the user’s meaning. Therefore, detecting inappropriate responses from a chatbot is a critical issue. Several studies based on annotated datasets have investigated the problem of inappropriate responses from a chatbots perspective without considering the user’s perspective. Understanding the context of the conversation is an important point in determining whether a response is appropriate or inappropriate. Sentiment analysis is a natural language processing task that supports mining in user behavior. Therefore, we propose an intelligent framework that combines automated sentiment scoring and a word embedding model to detect the quality of chatbot responses considering the end-user’s point of view. We find our model achieves higher quality results than logistic regression.
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