71 citations · 106 across the 8 of their papers we have counts for
8 papers
NumGPT: Improving Numeracy Ability of Generative Pre-trained Models
Zhihua Jin, Xin Jiang, Xingbo Wang +4
Existing generative pre-trained language models (e.g., GPT) focus on modeling the language structure and semantics of general texts. However, those models do not consider the numer…
Towards Efficient Post-training Quantization of Pre-trained Language Models
Haoli Bai, Lu Hou, Lifeng Shang +3
Network quantization has gained increasing attention with the rapid growth of large pre-trained language models~(PLMs). However, most existing quantization methods for PLMs follow…
Improving Unsupervised Question Answering via Summarization-Informed Question Generation
Chenyang Lyu, Lifeng Shang, Yvette Graham +3
Question Generation (QG) is the task of generating a plausible question for a given <passage, answer> pair. Template-based QG uses linguistically-informed heuristics to transform d…
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation
Xin Wang, Yasheng Wang, Fei Mi +7
Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code inte…
Generate & Rank: A Multi-task Framework for Math Word Problems
Jianhao Shen, Yichun Yin, Lin Li +4
Math word problem (MWP) is a challenging and critical task in natural language processing. Many recent studies formalize MWP as a generation task and have adopted sequence-to-seque…
Integrating Regular Expressions with Neural Networks via DFA
Shaobo Li, Qun Liu, Xin Jiang +5
Human-designed rules are widely used to build industry applications. However, it is infeasible to maintain thousands of such hand-crafted rules. So it is very important to integrat…