| Masked Language Model Scoring J Salazar, D Liang, TQ Nguyen, K Kirchhoff ACL 2020, 2019 | 475 | 2019 |
| Improve Transformer Models with Better Relative Position Embeddings Z Huang, D Liang, P Xu, B Xiang Findings of EMNLP 2020, 2020 | 117 | 2020 |
| Learning noise-invariant representations for robust speech recognition D Liang, Z Huang, ZC Lipton IEEE SLT 2018, 2018 | 59 | 2018 |
| Xlm-v: Overcoming the vocabulary bottleneck in multilingual masked language models D Liang, H Gonen, Y Mao, R Hou, N Goyal, M Ghazvininejad, ... EMNLP 2023, 2023 | 37 | 2023 |
| Embedding-based zero-shot retrieval through query generation D Liang, P Xu, S Shakeri, CN Santos, R Nallapati, Z Huang, B Xiang | 37 | 2020 |
| TRANS-BLSTM: Transformer with bidirectional LSTM for language understanding Z Huang, P Xu, D Liang, A Mishra, B Xiang | 34 | 2020 |
| Decoding and diversity in machine translation N Roberts, D Liang, G Neubig, ZC Lipton Resistance AI Workshop (NeurIPS 2020), 2020 | 27 | 2020 |
| The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants L Bandarkar, D Liang, B Muller, M Artetxe, SN Shukla, D Husa, N Goyal, ... | 21 | 2023 |
| Attention-guided generative models for extractive question answering P Xu, D Liang, Z Huang, B Xiang | 12 | 2021 |
| Deep Automated Multi-task Learning D Liang, Y Shu IJCNLP 2017, 2017 | 8 | 2017 |
| Invariant representation learning for robust deep networks J Salazar, D Liang, Z Huang, Z Lipton | 5 | 2018 |
| A Study on Knowledge Distillation from Weak Teacher for Scaling Up Pre-trained Language Models H Lee, R Hou, J Kim, D Liang, SJ Hwang, A Min Findings of ACL 2023, 2023 | 4 | 2023 |
| For distillation, tokens are not all you need M Raman, P Mani, D Liang, Z Lipton NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following, 2023 | 2 | 2023 |
| Adaptable Claim Rewriting with Offline Reinforcement Learning for Effective Misinformation Discovery A Kazemi, A Abzaliev, N Deng, R Hou, D Liang, SA Hale, V Pérez-Rosas, ... AACL 2023, 2022 | 2 | 2022 |
| Automated Multi-task Learning D Liang University of California, San Diego, 2017 | 2 | 2017 |
| RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training J Kim, Y Mao, R Hou, H Yu, D Liang, P Fung, Q Wang, F Feng, L Huang, ... Findings of the Association for Computational Linguistics: EMNLP 2023, 3412-3444, 2023 | 1 | 2023 |
| Generating Hashtags for Short-form Videos with Guided Signals T Yu, H Yu, D Liang, Y Mao, S Nie, PY Huang, M Khabsa, P Fung, ... Proceedings of the 61st Annual Meeting of the Association for Computational …, 2023 | 1 | 2023 |
| Multiplicative Position-aware Transformer Models for Language Understanding Z Huang, D Liang, P Xu, B Xiang | 1 | 2021 |
| Co-training and Co-distillation for Quality Improvement and Compression of Language Models H Lee, R Hou, J Kim, D Liang, H Zhang, SJ Hwang, A Min Findings of EMNLP 2023, 2023 | | 2023 |
| Robustifying Language Models via Adversarial Training with Masked Gradient J Kim, Y Mao, R Hou, H Yu, D Liang, P Fung, Q Wang, M Khabsa Findings of EMNLP 2023, 2023 | | 2023 |
zhiheng huangAmazon AWS AI在 amazon.com 的电子邮件经过验证
