Papers by Nastaran Babanejad
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks (2020.acl-main)
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| Challenge: | Affective tasks such as sentiment analysis, emotion classification and sarcasm detection have enjoyed great popularity in recent years. |
| Approach: | They conduct a comprehensive analysis of the role of preprocessing techniques in affective analysis based on word vector models. |
| Outcome: | The proposed model is the first of its kind and provides useful insights on the role of each preprocessing technique when applied at the training phase, commonly ignored in pretrained word vector models, and/or at the downstream task phase. |
Affective and Contextual Embedding for Sarcasm Detection (2020.coling-main)
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| Challenge: | Existing methods to detect sarcasm from text lack vocal intonation or facial gestures in textual data. |
| Approach: | They propose two deep neural network models for sarcasm detection that extend the architecture of BERT by incorporating both affective and contextual features. |
| Outcome: | The proposed models outperform state-of-the-art models on different datasets with significant margins. |