18 citations · 26 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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…
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
Haoyu Wang, Ming Tan, Mo Yu +5
Most approaches to extraction multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this ma…
Attentive Pooling Networks
Cicero dos Santos, Ming Tan, Bing Xiang +1
In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training. In the context of pair-wise ranking or classification with neural…