Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Zero-Shot Dialogue Disentanglement by Self-Supervised Entangled Response
  Selection   Dialogue disentanglement aims to group utterances in a long and
multi-participant dialogue into threads. This is useful for discourse analysis
and downstream applications such as dialogue response selection, where it can
be the first step to construct a clean context/response set. Unfortunately,
labeling all~\emph{reply-to} links takes quadratic effort w.r.t the number of
utterances: an annotator must check all preceding utterances to identify the
one to which the current utterance is a reply. In this paper, we are the first
to propose a~\textbf{zero-shot} dialogue disentanglement solution. Firstly, we
train a model on a multi-participant response selection dataset harvested from
the web which is not annotated; we then apply the trained model to perform
zero-shot dialogue disentanglement. Without any labeled data, our model can
achieve a cluster F1 score of 25. We also fine-tune the model using various
amounts of labeled data. Experiments show that with only 10\% of the data, we
achieve nearly the same performance of using the full dataset\footnote{Code is
released at
\url{https://github.com/chijames/zero_shot_dialogue_disentanglement}}.

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The categories of this paper are: math
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an