| Speaker-aware BERT for multi-turn response selection in retrieval-based chatbots JC Gu, T Li, Q Liu, ZH Ling, Z Su, S Wei, X Zhu Proceedings of the 29th ACM International Conference on Information …, 2020 | 162 | 2020 |
| Conversation-and tree-structure losses for dialogue disentanglement T Li, JC Gu, ZH Ling, Q Liu Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and …, 2022 | 25* | 2022 |
| A short study on compressing decoder-based language models T Li, YE Mesbahi, I Kobyzev, A Rashid, A Mahmud, N Anchuri, ... arXiv preprint arXiv:2110.08460, 2021 | 21 | 2021 |
| Several experiments on investigating pretraining and knowledge-enhanced models for natural language inference T Li, X Zhu, Q Liu, Q Chen, Z Chen, S Wei arXiv preprint arXiv:1904.12104, 2019 | 20 | 2019 |
| SPDF: Sparse pre-training and dense fine-tuning for large language models V Thangarasa, A Gupta, W Marshall, T Li, K Leong, D DeCoste, S Lie, ... Uncertainty in Artificial Intelligence, 2134-2146, 2023 | 19 | 2023 |
| Pre-trained and attention-based neural networks for building noetic task-oriented dialogue systems JC Gu, T Li, Q Liu, X Zhu, ZH Ling, YP Ruan arXiv preprint arXiv:2004.01940, 2020 | 14 | 2020 |
| Deep contextualized utterance representations for response selection and dialogue analysis JC Gu, T Li, ZH Ling, Q Liu, Z Su, YP Ruan, X Zhu IEEE/ACM Transactions on Audio, Speech, and Language Processing 29, 2443-2455, 2021 | 11 | 2021 |
| Learning to retrieve entity-aware knowledge and generate responses with copy mechanism for task-oriented dialogue systems CH Tan, X Yang, Z Zheng, T Li, Y Feng, JC Gu, Q Liu, D Liu, ZH Ling, ... arXiv preprint arXiv:2012.11937, 2020 | 10 | 2020 |
| How to select one among all? an empirical study towards the robustness of knowledge distillation in natural language understanding T Li, A Rashid, A Jafari, P Sharma, A Ghodsi, M Rezagholizadeh Findings of the Association for Computational Linguistics: EMNLP 2021, 750-762, 2021 | 8* | 2021 |
| Do we need Label Regularization to Fine-tune Pre-trained Language Models? I Kobyzev, A Jafari, M Rezagholizadeh, T Li, A Do-Omri, P Lu, P Poupart, ... arXiv preprint arXiv:2205.12428, 2022 | 2 | 2022 |
| Towards understanding label regularization for fine-tuning pre-trained language models I Kobyzev, A Jafari, M Rezagholizadeh, T Li, A Do-Omri, P Lu, A Ghodsi, ... arXiv preprint arXiv:2205.12428, 2022 | 1 | 2022 |
| Unsupervised Pre-training with Structured Knowledge for Improving Natural Language Inference X Yang, X Zhu, Z Shi, T Li arXiv preprint arXiv:2109.03941, 2021 | 1 | 2021 |
| Have You Made a Decision? Where? A Pilot Study on Interpretability of Polarity Analysis Based on Advising Problem T Li, JC Gu, H Liu, Q Liu, ZH Ling, Z Su, X Zhu ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | | 2021 |
| SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models (Supplementary Material) V Thangarasa, A Gupta, W Marshall, T Li, K Leong, D DeCoste, S Lie, ... | | |
| Developing Better Models for Dialogue Threads and Responses T Li | | |
