Name
Abstract | ||
|---|---|---|
Human agents in technical customer support provide users with instructional answers to solve a task that would otherwise require a lot of time, money, energy, physical costs. Developing a dialogue system in this domain is challenging due to the broad variety of user questions. Moreover, user questions are noisy (for example, spelling mistakes), redundant and have various natural language expressions. In this work, we introduce a conversational system, MOLI (the name of our dialogue system), to solve customer questions by providing instructional answers from a knowledge base. Our approach combines models for question type and intent category classification with slot filling and a back-end knowledge base for filtering and ranking answers, and uses a dialog framework to actively query the user for missing information. For answer-ranking we find that sequential matching networks and neural multi-perspective sentence similarity networks clearly outperform baseline models, achieving a 43% error reduction. The end-to-end P@1(Precision at top 1) of MOLI was 0.69 and the customers' satisfaction was 0.73. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/info10020063 | INFORMATION |
Keywords | Field | DocType |
question and answer, dialog systems, semantic matching | Dialog box,Conversation,Ranking,Expression (mathematics),Computer science,Human–computer interaction,Natural language,Artificial intelligence,Spelling,Knowledge base,Machine learning,Semantic matching | Journal |
Volume | Issue | ISSN |
10 | 2 | 2078-2489 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoguang Zhao | 1 | 0 | 1.01 |
| Jianyu Zhao | 2 | 1 | 2.42 |
| Yang Li | 3 | 659 | 125.00 |
| Christoph Alt | 4 | 3 | 4.10 |
| Robert Schwarzenberg | 5 | 0 | 0.68 |
| Leonhard Hennig | 6 | 72 | 10.62 |
| Stefan Schaffer | 7 | 0 | 0.34 |
| Sven Schmeier | 8 | 66 | 17.00 |
| Changjian Hu | 9 | 75 | 10.82 |
| Feiyu Xu | 10 | 450 | 48.79 |