Name
Abstract | ||
|---|---|---|
This paper presents a predictive study on the progress of conversations. Specifically, we estimate the residual life for conversations, which is defined as the count of new turns to occur in a conversation thread. While most previous work focus on coarse-grained estimation that classifies the number of coming turns into two categories, we study fine-grained categorization for varying lengths of residual life. To this end, we propose a hierarchical neural model that jointly explores indicative representations from the content in turns and the structure of conversations in an end-to-end manner. Extensive experiments on both human-human and human-machine conversations demonstrate the superiority of our proposed model and its potential helpfulness in chatbot response selection. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/SLT48900.2021.9383544 | 2021 IEEE SPOKEN LANGUAGE TECHNOLOGY WORKSHOP (SLT) |
Keywords | DocType | ISSN |
Conversation Understanding, Dialogue System, Social Computing, User Behavior Analysis, Natural Language Processing | Conference | 2639-5479 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zexin Lu | 1 | 0 | 0.34 |
| Jing Li | 2 | 48 | 9.03 |
| Yingyi Zhang | 3 | 1 | 1.37 |
| Haisong Zhang | 4 | 15 | 8.00 |