403 citations · 756 across the 9 of their papers we have counts for
9 papers
Exploring Continual Learning for Code Generation Models
Prateek Yadav, Qing Sun, Hantian Ding +9
Large-scale code generation models such as Codex and CodeT5 have achieved impressive performance. However, libraries are upgraded or deprecated very frequently and re-training larg…
ContraCLM: Contrastive Learning For Causal Language Model
Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad +9
Despite exciting progress in causal language models, the expressiveness of the representations is largely limited due to poor discrimination ability. To remedy this issue, we prese…
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization
Zheng Li, Zijian Wang, Ming Tan +5
Large-scale pre-trained sequence-to-sequence models like BART and T5 achieve state-of-the-art performance on many generative NLP tasks. However, such models pose a great challenge…
ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency
Deng Huang, Wenhao Wu, Weiwen Hu +6
We study self-supervised video representation learning, which is a challenging task due to 1) lack of labels for explicit supervision; 2) unstructured and noisy visual information.…
Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations
Ke Tran, Ming Tan
Modern conversational AI systems support natural language understanding for a wide variety of capabilities. While a majority of these tasks can be accomplished using a simple and f…
Out-of-Domain Detection for Low-Resource Text Classification Tasks
Ming Tan, Yang Yu, Haoyu Wang +4
Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task. The goal is to detect the OOD cases with limited in-domain (ID) training da…