Papers with transformer-based pre-trained models
Quantum-inspired Language Model with Lindblad Master Equation and Interference Measurement for Sentiment Analysis (2024.naacl-long)
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| Challenge: | Quantum-inspired models have demonstrated superior performance in many downstream language tasks, such as question answering and sentiment analysis. |
| Approach: | They propose a quantum-inspired neural network that integrates the Lindblad Master Equation to model the evolution process and the interferometry to the measurement process, providing more physical meaning to strengthen the interpretability. |
| Outcome: | The proposed model outperforms existing models on sentiment analysis datasets and shows that it is more accurate and performs better than existing models. |
An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next (2021.findings-emnlp)
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Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah, Dragomir Radev
| Challenge: | Existing models for dialogue summarization focus on extracting the main events of short conversations, but real-world dialogues are difficult to train. |
| Approach: | They propose three strategies to deal with the lengthy input problem and locate relevant information using long dialogue datasets. |
| Outcome: | The retrieve-then-summarize pipeline models yield the best performance on three long dialogue datasets. |