natural-language-processing-7
Conversation Disentanglement
3260 papers • 126 benchmarks • 313 datasets
Automatic disentanglement could be used to provide more interpretable results when searching over chat logs, and to help users understand what is happening when they join a channel. Source: Kummerfeld et al.
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in conversation-disentanglement-14
Trend
Dataset
Best Model
Actions
irc-disentanglement
irc-disentanglement
Linux IRC (Ch2 Kummerfeld)
Linux IRC (Ch2 Kummerfeld)
Libraries
iUse these libraries to find conversation-disentanglement-14 models and implementations
Datasets
Subtasks
No subtasks available.
Most implemented papers
A Large-Scale Corpus for Conversation Disentanglement
Walter S. Lasecki, R. Chulaka Gunasekara, Jonathan K. Kummerfeld, S. R. Gouravajhala, Joseph Peper, V. Athreya, Jatin Ganhotra, Siva Sankalp Patel, L. Polymenakos•Wed Oct 24 2018
A new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure is created, which is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and theFirst to include context.