Papers by Ehsan Hosseini-Asl
Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models (2021.eacl-main)
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| Challenge: | Existing calibration methods rescale posterior distributions of classifiers after training. |
| Approach: | They propose to use a noise contrastive estimation technique to train an energy-based model during finetuning of pretrained text encoders. |
| Outcome: | The proposed model can reach a better calibration competitive to strong baselines with little or no loss in accuracy. |
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis (2022.findings-naacl)
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| Challenge: | Pre-trained language models are often used to achieve state-of-the-art results . eval paper shows that generative language model can handle joint and multi-task settings . |
| Approach: | They propose to reformulate extraction and prediction tasks into a sequence generation task . they propose a generative language model with unidirectional attention that learns to accomplish the tasks via language generation . |
| Outcome: | The proposed model outperforms the state-of-the-art in few-shot and full-shot settings. |
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems (P19-1)
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| Challenge: | Existing approaches to dialogue state tracking are dependent on domain ontology and lack of sharing knowledge across domains. |
| Approach: | They propose a transferable dialogue state generator that generates dialogue states from utterances using copy mechanism. |
| Outcome: | Empirical results show that TRADE achieves state-of-the-art 48.62% joint goal accuracy for the five domains of MultiWOZ. |