Online Conversation Disentanglement with Pointer Networks
release_555fv2rufvdibkca42y3myqjuy
by
Tao Yu, Shafiq Joty
2020
Abstract
Huge amounts of textual conversations occur online every day, where multiple
conversations take place concurrently. Interleaved conversations lead to
difficulties in not only following the ongoing discussions but also extracting
relevant information from simultaneous messages. Conversation disentanglement
aims to separate intermingled messages into detached conversations. However,
existing disentanglement methods rely mostly on handcrafted features that are
dataset specific, which hinders generalization and adaptability. In this work,
we propose an end-to-end online framework for conversation disentanglement that
avoids time-consuming domain-specific feature engineering. We design a novel
way to embed the whole utterance that comprises timestamp, speaker, and message
text, and proposes a custom attention mechanism that models disentanglement as
a pointing problem while effectively capturing inter-utterance interactions in
an end-to-end fashion. We also introduce a joint-learning objective to better
capture contextual information. Our experiments on the Ubuntu IRC dataset show
that our method achieves state-of-the-art performance in both link and
conversation prediction tasks.
In text/plain
format
Archived Files and Locations
application/pdf 1.1 MB
file_uk7oy7lydffvxfhxqugt4lc6a4
|
arxiv.org (repository) web.archive.org (webarchive) |
2010.11080v1
access all versions, variants, and formats of this works (eg, pre-prints)