# Active Learning and Multi-label Classification for Ellipsis and Coreference Detection in Conversational Question-Answering

Source: [Active Learning and Multi-label Classification for Ellipsis and Coreference Detection in Conversational Question-Answering](<https://www.sota2.com/research/paper/68fa6cf21e209131cea36b58>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:16:31.054Z

Authors: Quentin Brabant, Lina Maria Rojas-Barahona, Claire Gardent

Published: 2022-07-07T08:14:54.000Z

## Abstract

In human conversations, ellipsis and coreference are commonly occurring linguistic phenomena. Although these phenomena are a mean of making human-machine conversations more fluent and natural, only few dialogue corpora contain explicit indications on which turns contain ellipses and/or coreferences. In this paper we address the task of automatically detecting ellipsis and coreferences in conversational question answering. We propose to use a multi-label classifier based on DistilBERT. Multi-label classification and active learning are employed to compensate the limited amount of labeled data. We show that these methods greatly enhance the performance of the classifier for detecting these phenomena on a manually labeled dataset.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2207.03145>)

[PDF](<https://arxiv.org/pdf/2207.03145.pdf>)