| 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. |
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Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Models (2025.emnlp-main)
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| Challenge: | Recent efforts to develop lightweight and practical sentiment analysis models are limited by manual instruction and large-scale user texts. |
| Approach: | They propose a framework for sentiment analysis that uses attribute-based instruction construction and difficulty-based data filtering to distill knowledge. |
| Outcome: | The proposed framework outperforms baseline methods in data efficiency and performance. |
Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation (2021.emnlp-main)
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| Challenge: | stance detection is a method to determine whether a text author is in favor of, against or neutral toward a specific target. |
| Approach: | They propose a method that applies instance-specific temperature scaling to the teacher and student predictions. |
| Outcome: | The proposed method outperforms the state-of-the-art on all datasets and on multiple datasets. |
Targeted Distillation for Sentiment Analysis (2025.emnlp-main)
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| Challenge: | Recent studies demonstrate that large language models exhibit remarkable capabilities and achieve state-of-the-art performance in diverse sentiment analysis tasks. |
| Approach: | They propose a distillation framework that decouples knowledge from alignment and introduces a sentiment analysis benchmark that covers a diverse set of tasks. |
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Modeling Empathy and Distress in Reaction to News Stories (D18-1)
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| Challenge: | a recent work on empathy prediction has underestimated the complexity of the phenomenon and lacks a shared corpus. authors present a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. |
| Approach: | They propose a method which captures empathy assessments by the writer of a statement using multi-item scales. |
| Outcome: | The proposed method distinguishes between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. |
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs (N19-1)
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| Challenge: | Existing methods to categorize sentiments and emotions in text are limited. |
| Approach: | They propose to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. |
| Outcome: | The proposed method improves on a recently published dataset for categorizing human needs. |
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. |
Distilling Knowledge for Search-based Structured Prediction (P18-1)
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| Challenge: | Existing studies have focused on the performance of structured prediction models, but they are often limited by the ambiguities of the reference policy. |
| Approach: | They propose to distill an ensemble of multiple models trained with different initializations into a single model and use it to explore the search space. |
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Distilling Calibrated Knowledge for Stance Detection (2023.findings-acl)
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| Challenge: | Existing methods for stance detection ignore meaningful signals among categories offered by hard labels. |
| Approach: | They propose to use knowledge distillation to calibrate teacher predictions in each generation step. |
| Outcome: | The proposed method can calibrate teacher predictions in each generation step and improves stance detection accuracy. |
EmpathicStories++: A Multimodal Dataset for Empathy Towards Personal Experiences (2024.findings-acl)
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Jocelyn Shen, Yubin Kim, Mohit Hulse, Wazeer Zulfikar, Sharifa Alghowinem, Cynthia Breazeal, Hae Park
| Challenge: | Existing datasets for empathy modeling are limited in the ways they are not captured in the wild. |
| Approach: | They propose a multimodal dataset for empathy during personal experience sharing that contains 53 hours of video, audio, and text data of 41 participants. |
| Outcome: | The EmpathicStories++ dataset contains 53 hours of video, audio, and text data of 41 participants sharing vulnerable experiences and reading empathically resonant stories with an AI agent. |
XtremeDistil: Multi-stage Distillation for Massive Multilingual Models (2020.acl-main)
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| Challenge: | Existing work on pre-trained language models focuses on reducing the size of these models into shallow ones. |
| Approach: | They propose a knowledge distillation technique that leverages teacher internal representations to reduce the size of pre-trained language models. |
| Outcome: | The proposed method outperforms previous methods in multilingual Named Entity Recognition (NER) it reduces the size of teacher models by 35x while retaining 95% of its F1 score. |