Summary of Experimental Evaluation Of Machine Learning Models For Goal-oriented Customer Service Chatbot with Pipeline Architecture, by Nurul Ain Nabilah Mohd Isa et al.
Experimental Evaluation of Machine Learning Models for Goal-oriented Customer Service Chatbot with Pipeline Architecture
by Nurul Ain Nabilah Mohd Isa, Siti Nuraishah Agos Jawaddi, Azlan Ismail
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the integration of machine learning (ML) into customer service chatbots, aiming to improve their understanding and response capabilities. However, ML models may appear artificial, affecting customer experience. To optimize performance, a tailored experimental evaluation approach is crucial, focusing on three key components: Natural Language Understanding (NLU), dialogue management (DM), and Natural Language Generation (NLG). The authors emphasize individual assessment of ML models to determine optimal hyperparameters and evaluate candidate models for each component. Specifically, they test BERT and LSTM for NLU, DQN and DDQN for DM, and GPT-2 and DialoGPT for NLG. The results show that BERT excels in intent detection, LSTM is superior for slot filling, DDQN outperforms DQN for DM, and GPT-2 surpasses DialoGPT for NLG. These findings provide a benchmark for future research on developing and optimizing customer service chatbots. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make customer service chatbots better at understanding and helping people. Sometimes these chatbots can seem too robotic, which is not good for customers. To make them better, the researchers created a special way to test different parts of the chatbot, like how it understands what people say, how it talks back, and how it generates responses. They tested different machine learning models for each part and found that some worked much better than others. For example, one model was great at understanding what people want, while another was good at talking about topics without sounding too robotic. The results will help other researchers make even better chatbots in the future. |
Keywords
» Artificial intelligence » Bert » Gpt » Intent detection » Language understanding » Lstm » Machine learning
