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Conference Papers Year : 2022

Classifying the response space of questions: A machine learning approach

Abstract

The main goal of this work is to conduct a pi-lot study on the automatic classification of the response space of questions in English. We aim for a relatively fine-grained understand-ing of the learning problem of this response space; hence, we conducted classical machine learning studies to automatically identify dif-ferent response classes based on carefully de-signed features. Moreover, we compared the results from feature-based classical machine learning algorithms to the classification results obtained from a large-scale pre-trained BERT language model. Experimental results show that the feature-based classical machine learn-ing algorithms can achieve performance results which are close to the results obtained by BERT model on this novel task. The overall trend of the classification results for each response class are also similar in both models. Learnability trends similar to corpus-based studies presented in previous literatures emerge.
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Dates and versions

hal-03992294 , version 1 (21-02-2023)

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  • HAL Id : hal-03992294 , version 1

Cite

Zulipiye Yusupujiang, Alafate Abulimiti, Jonathan Ginzburg. Classifying the response space of questions: A machine learning approach. SemDial 2022 - 26th Workshop on the Semantics and Pragmatics of Dialogue, Aug 2022, Dublin, Ireland. pp.59-69. ⟨hal-03992294⟩
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