Enhancing Discoverability in Enterprise Conversational Systems with Proactive Question Suggestions
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916523636948992 |
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| author | Shen, Xiaobin Lee, Daniel Ranjan, Sumit Harsha, Sai Sree Sevak, Pawan Li, Yunyao |
| author_facet | Shen, Xiaobin Lee, Daniel Ranjan, Sumit Harsha, Sai Sree Sevak, Pawan Li, Yunyao |
| contents | Enterprise conversational AI systems are becoming increasingly popular to assist users in completing daily tasks such as those in marketing and customer management. However, new users often struggle to ask effective questions, especially in emerging systems with unfamiliar or evolving capabilities. This paper proposes a framework to enhance question suggestions in conversational enterprise AI systems by generating proactive, context-aware questions that try to address immediate user needs while improving feature discoverability. Our approach combines periodic user intent analysis at the population level with chat session-based question generation. We evaluate the framework using real-world data from the AI Assistant for Adobe Experience Platform (AEP), demonstrating the improved usefulness and system discoverability of the AI Assistant. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10933 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Discoverability in Enterprise Conversational Systems with Proactive Question Suggestions Shen, Xiaobin Lee, Daniel Ranjan, Sumit Harsha, Sai Sree Sevak, Pawan Li, Yunyao Computation and Language Enterprise conversational AI systems are becoming increasingly popular to assist users in completing daily tasks such as those in marketing and customer management. However, new users often struggle to ask effective questions, especially in emerging systems with unfamiliar or evolving capabilities. This paper proposes a framework to enhance question suggestions in conversational enterprise AI systems by generating proactive, context-aware questions that try to address immediate user needs while improving feature discoverability. Our approach combines periodic user intent analysis at the population level with chat session-based question generation. We evaluate the framework using real-world data from the AI Assistant for Adobe Experience Platform (AEP), demonstrating the improved usefulness and system discoverability of the AI Assistant. |
| title | Enhancing Discoverability in Enterprise Conversational Systems with Proactive Question Suggestions |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.10933 |