Prati
Ernie Chang
Ernie Chang
Facebook Inc. ; Saarland University
Potvrđena adresa e-pošte na fb.com
Naslov
Citirano
Citirano
Godina
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
382020
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
382020
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
182018
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
172020
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
162021
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
162020
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
162017
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
132020
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
132020
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
112021
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
112021
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
102019
On Training Instance Selection for Few-Shot Neural Text Generation
E Chang, X Shen, HS Yeh, V Demberg
Proceedings of ACL 2021, 2021
92021
The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation
E Chang, X Shen, A Marin, V Demberg
Proceedings of the 14th INLG, https://aclanthology.org/2021.inlg-1.36/, 2021
42021
Safe Handover in Mixed-Initiative Control for Cyber-Physical Systems
F Wiehr, A Hirsch, F Daiber, A Kruger, A Kovtunova, S Borgwardt, ...
In Proceedings of Workshop at CHI, 2020
42020
Why Do I Have to Take Over Control? Evaluating Safe Handovers with Advance Notice and Explanations in HAD
F Wiehr, A Hirsch, L Schmitz, N Knieriemen, A Krüger, A Kovtunova, ...
Proceedings of the 2021 International Conference on Multimodal Interaction …, 2021
12021
Logic-Guided Neural Utterance Generation from Drone Sensory Data
S Borgwardt, E Chang, K Chapman, V Demberg, A Kovtunova, HS Yeh
34th International Workshop on Description Logics, 2021
12021
Time-Aware Ancient Chinese Text Translation and Inference
E Chang, YT Shiue, HS Yeh, V Demberg
LChange @ ACL 2021, 2021
12021
A Generative-Discriminative Framework for Title Generation in the E-commerce Domain
E Chang
https://digital.lib.washington.edu/researchworks/handle/1773/41813, 2018
12018
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
2022
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