Name
Abstract | ||
|---|---|---|
We present an unsupervised model of dialogue act sequences in conversation. By modeling topical themes as transitioning more slowly than dialogue acts in conversation, our model de-emphasizes content-related words in order to focus on conversational function words that signal dialogue acts. We also incorporate speaker tendencies to use some acts more than others as an additional predictor of dialogue act prevalence beyond temporal dependencies. According to the evaluation presented on two dissimilar corpora, the CNET forum and NPS Chat corpus, the effectiveness of each modeling assumption is found to vary depending on characteristics of the data. De-emphasizing content-related words yields improvement on the CNET corpus, while utilizing speaker tendencies is advantageous on the NPS corpus. The components of our model complement one another to achieve robust performance on both corpora and outperform state-of-the-art baseline models. |
Year | Venue | Field |
|---|---|---|
2017 | EMNLP | Content word,Dialogue acts,Computer science,Filter (signal processing),Natural language processing,Artificial intelligence,Speaker diarisation |
DocType | Volume | Citations |
Conference | 2017 | 1 |
PageRank | References | Authors |
0.38 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yohan Jo | 1 | 281 | 14.22 |
| Michael Yoder | 2 | 1 | 1.06 |
| Hyeju Jang | 3 | 7 | 2.92 |
| Rosé Carolyn | 4 | 2126 | 222.80 |