Papers with sample representations
Learn to Adapt for Generalized Zero-Shot Text Classification (2022.acl-long)
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| Challenge: | Existing methods for generalized zero-shot text classification generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes. |
| Approach: | They propose a network that trains an adaptive classifier by using both seen and virtual unseen classes to simulate a generalized zero-shot learning scenario. |
| Outcome: | The proposed model outperforms several previous approaches on five text classification datasets. |
LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization (2025.coling-main)
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| Challenge: | Existing methods to generate negative summaries are expensive and lack the capacity to generate large data sets. |
| Approach: | They propose a data augmentation framework based on LArge and Small language models for debiaSing opinion summarization that generates a small number of synthesized negative reviews by rewriting the positive text via a large language model. |
| Outcome: | The proposed framework can generate large numbers of negative reviews by rewriting the positive text using a large language model and training a disentangle reconstruction model based on the generated data. |
EmoTrans: Emotional Transition-based Model for Emotion Recognition in Conversation (2024.lrec-main)
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| Challenge: | Emotions are causally transmitted among communication participants, facilitating comprehension of intricate changes in emotional states during the conversation. |
| Approach: | They propose an Emotional Transition-based Emotion Recognizer that captures ET features in an emotional conversation by concatenating the most recent utterances with their corresponding speakers. |
| Outcome: | The proposed model is sensitive to emotions and captures ET features in the sample. |