You Talking to Me? A Corpus and Algorithm for Conversation Disentanglement
When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat (IRC) dialogue in which the various conversations have been manually disentangled, and evaluate annotator reliability. This is, to our knowledge, the first such corpus for internet chat. We propose a graph-theoretic model for disentanglement, using discourse-based features which have not been previously applied to this task. The model’s predicted disentanglements are highly correlated with manual annotations.
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Datasets
irc-disentanglement
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Conversation Disentanglement | irc-disentanglement | Linear | VI | 82.1 | # 4 | |
| 1-1 | 51.4 | # 3 | ||||
| P | 12.1 | # 5 | ||||
| R | 21.5 | # 4 | ||||
| F | 15.5 | # 5 | ||||
| Conversation Disentanglement | Linux IRC (Ch2 Elsner) | Linear | 1-1 | 53.1 | # 1 | |
| Local | 81.9 | # 1 | ||||
| Shen F-1 | 55.1 | # 1 | ||||
| Conversation Disentanglement | Linux IRC (Ch2 Kummerfeld) | Linear | 1-1 | 59.7 | # 1 | |
| Local | 80.8 | # 1 | ||||
| Shen F-1 | 63.0 | # 1 |
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