| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 1019 | 2022 |
| Llm-qat: Data-free quantization aware training for large language models Z Liu, B Oguz, C Zhao, E Chang, P Stock, Y Mehdad, Y Shi, ... arXiv preprint arXiv:2305.17888, 2023 | 146 | 2023 |
| Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence X Shen, E Chang, H Su, J Zhou, D Klakow arXiv preprint arXiv:2005.01096, 2020 | 60 | 2020 |
| Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence X Shen, E Chang, H Su, J Zhou, D Klakow Proceedings of ACL 2020, 2020 | 60 | 2020 |
| Neural Data-to-text Generation with LM-based Text Augmentation E Chang, X Shen, D Zhu, V Demberg, H Su Proceedings of EACL 2021, 2021 | 49 | 2021 |
| On Training Instance Selection for Few-Shot Neural Text Generation E Chang, X Shen, HS Yeh, V Demberg Proceedings of ACL 2021, 2021 | 36 | 2021 |
| A few thousand translations go a long way! leveraging pre-trained models for african news translation DI Adelani, JO Alabi, A Fan, J Kreutzer, X Shen, M Reid, D Ruiter, ... arXiv preprint arXiv:2205.02022, 2022 | 35 | 2022 |
| MovieChats: Chat like Humans in a Closed Domain H Su, X Shen, Z Xiao, Z Zhang, E Chang, C Zhang, C Niu, J Zhou Proceedings of EMNLP 2020, 6605-6619, 2020 | 35 | 2020 |
| Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning E Chang, HS Yeh, V Demberg Proceedings of EACL 2021, 2021 | 32 | 2021 |
| Mobilellm: Optimizing sub-billion parameter language models for on-device use cases Z Liu, C Zhao, F Iandola, C Lai, Y Tian, I Fedorov, Y Xiong, E Chang, ... arXiv preprint arXiv:2402.14905, 2024 | 31 | 2024 |
| Generating e-commerce product titles and predicting their quality JGC de Souza, M Kozielski, P Mathur, E Chang, M Guerini, M Negri, ... Proceedings of INLG, 233-243, 2018 | 28 | 2018 |
| Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling E Chang, V Demberg, A Marin Proceedings of EACL 2021, 2021 | 24 | 2021 |
| Unsupervised Pidgin Text Generation By Pivoting English Data and Self-Training E Chang, D Adelani, X Shen, V Demberg In Proceedings of Workshop at ICLR, 2020 | 22 | 2020 |
| Neobility at SemEval-2017 Task 1: An attention-based sentence similarity model. WL Zhuang, E Chang In Proceedings of SemEval-2017 at ACL 2017., 2017 | 19 | 2017 |
| DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool E Chang, J Caplinger, A Marin, X Shen, V Demberg Proceedings of COLING 2020 (Best Demo Paper Award), 12-17, 2020 | 18 | 2020 |
| DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool E Chang, J Caplinger, A Marin, X Shen, V Demberg arXiv preprint arXiv:2010.04141, 2020 | 18 | 2020 |
| Guyo Jarso, Oreen Yousuf, Andre Niyongabo Rubungo, Gilles Hacheme, Eric Peter Wairagala, Muhammad Umair Nasir D Adelani, J Alabi, A Fan, J Kreutzer, X Shen, M Reid, D Ruiter, D Klakow, ... | 13 | 2022 |
| Time-Aware Ancient Chinese Text Translation and Inference E Chang, YT Shiue, HS Yeh, V Demberg LChange @ ACL 2021, 2021 | 13 | 2021 |
| Improving language generation from feature-rich tree-structured data with relational graph convolutional encoders X Hong, E Chang, V Demberg Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR …, 2019 | 13 | 2019 |
| Mdia: A benchmark for multilingual dialogue generation in 46 languages Q Zhang, X Shen, E Chang, J Ge, P Chen arXiv preprint arXiv:2208.13078, 2022 | 12 | 2022 |
Vera DembergSaarland University, Computer Science, Saarland Informatics CampusVerified email at coli.uni-saarland.de
