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Ting-En Lin
Ting-En Lin
Alibaba Group, Tongyi
E-mail confirmado em alibaba-inc.com - Página inicial
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Citado por
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Ano
Deep Unknown Intent Detection with Margin Loss
TE Lin, H Xu
Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019
1572019
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition
G Hu, TE Lin, Y Zhao, G Lu, Y Wu, Y Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022
1302022
Discovering new intents with deep aligned clustering
H Zhang, H Xu, TE Lin, R Lyu
Proceedings of the AAAI Conference on Artificial Intelligence 35 (16), 14365 …, 2021
1162021
Deep open intent classification with adaptive decision boundary
H Zhang, H Xu, TE Lin
Proceedings of the AAAI Conference on Artificial Intelligence 35 (16), 14374 …, 2021
1142021
Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement
TE Lin, H Xu, H Zhang
The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), 2020
1122020
A post-processing method for detecting unknown intent of dialogue system via pre-trained deep neural network classifier
TE Lin, H Xu
Knowledge-Based Systems 186, 104979, 2019
442019
Automated machine learning for Internet of Things
CM Chung, CC Chen, WP Shih, TE Lin, RJ Yeh, I Wang
2017 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW …, 2017
292017
Spokenwoz: A large-scale speech-text benchmark for spoken task-oriented dialogue agents
S Si, W Ma, H Gao, Y Wu, TE Lin, Y Dai, H Li, R Yan, F Huang, Y Li
Advances in Neural Information Processing Systems 36, 2024
25*2024
A survey on self-evolution of large language models
Z Tao, TE Lin, X Chen, H Li, Y Wu, Y Li, Z Jin, F Huang, D Tao, J Zhou
arXiv preprint arXiv:2404.14387, 2024
222024
Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems
TE Lin, Y Wu, F Huang, L Si, J Sun, Y Li
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022
212022
Unsupervised dialogue topic segmentation with topic-aware contrastive learning
H Gao, R Wang, TE Lin, Y Wu, M Yang, F Huang, Y Li
Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023
17*2023
Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment
T Yu, H Gao, TE Lin, M Yang, Y Wu, W Ma, C Wang, F Huang, Y Li
Proceedings of the 61st Annual Meeting of the Association for Computational …, 2023
17*2023
Empathetic response generation via emotion cause transition graph
Y Qian, B Wang, TE Lin, Y Zheng, Y Zhu, D Zhao, Y Hou, Y Wu, Y Li
ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023
172023
Fortify the shortest stave in attention: Enhancing context awareness of large language models for effective tool use
Y Chen, A Lv, TE Lin, C Chen, Y Wu, F Huang, Y Li, R Yan
arXiv preprint arXiv:2312.04455, 2023
102023
Unisa: Unified generative framework for sentiment analysis
Z Li, TE Lin, Y Wu, M Liu, F Tang, M Zhao, Y Li
Proceedings of the 31st ACM International Conference on Multimedia, 6132-6142, 2023
102023
Multi-View Active Fine-Grained Visual Recognition
R Du, W Yu, H Wang, TE Lin, D Chang, Z Ma
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
8*2023
Deem: Diffusion models serve as the eyes of large language models for image perception
R Luo, Y Li, L Chen, W He, TE Lin, Z Liu, L Zhang, Z Song, X Xia, T Liu, ...
arXiv preprint arXiv:2405.15232, 2024
72024
Masked thought: Simply masking partial reasoning steps can improve mathematical reasoning learning of language models
C Chen, X Wang, TE Lin, A Lv, Y Wu, X Gao, JR Wen, R Yan, Y Li
arXiv preprint arXiv:2403.02178, 2024
72024
Constructive large language models alignment with diverse feedback
T Yu, TE Lin, Y Wu, M Yang, F Huang, Y Li
arXiv preprint arXiv:2310.06450, 2023
62023
Improving factual consistency of text summarization by adversarially decoupling comprehension and embellishment abilities of llms
H Feng, Y Fan, X Liu, TE Lin, Z Yao, Y Wu, F Huang, Y Li, Q Ma
arXiv preprint arXiv:2310.19347, 2023
52023
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