Papers by Mahshid Hosseini
Semi-Supervised Domain Adaptation for Emotion-Related Tasks (2023.findings-acl)
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| Challenge: | Semi-supervised domain adaptation (SSDA) is a model trained from a label-rich source domain to a new but related domain with a few labels of target data. |
| Approach: | They propose to decompose the semi-supervised domain adaptation framework into two subcomponents of unsupervised domain adaption (UDA) from the source to the target domain and semi-supervised learning (SSL) in the target. |
| Outcome: | The proposed method is based on the co-learning of multiple classifiers for computer vision tasks and is published in the journal Nature. |
Distilling Knowledge for Empathy Detection (2021.findings-emnlp)
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| Challenge: | Empathy is the link between self and others. |
| Approach: | They employ multi-task training with knowledge distillation to integrate knowledge from available resources to detect empathy from the natural language in different domains. |
| Outcome: | The proposed approach yields better results on an existing news-related empathy dataset compared to strong baselines. |
Calibrating Student Models for Emotion-related Tasks (2022.emnlp-main)
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| Challenge: | Knowledge distillation is an effective method to transfer knowledge from one network (a.k.a. teacher) to another (as student). |
| Approach: | They propose to use a mixup data augmentation technique to increase the accuracy of the model by providing better training signals to the student models. |
| Outcome: | The proposed method improves the calibration of student models while providing better training signals to the student models using training dynamics. |