252 citations · 2k across the 56 of their papers we have counts for
4 papers · 2 filters
Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation
Mingkai Deng, Bowen Tan, Zhengzhong Liu +2
Natural language generation (NLG) spans a broad range of tasks, each of which serves for specific objectives and desires different properties of generated text. The complexity make…
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Guangyi Liu, Zichao Yang, Tianhua Tao +6
Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss, which encourages an exact token-by-token match between a…
Efficient (Soft) Q-Learning for Text Generation with Limited Good Data
Han Guo, Bowen Tan, Zhengzhong Liu +2
Maximum likelihood estimation (MLE) is the predominant algorithm for training text generation models. This paradigm relies on direct supervision examples, which is not applicable t…
A Data-Centric Framework for Composable NLP Workflows
Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu +15
Empirical natural language processing (NLP) systems in application domains (e.g., healthcare, finance, education) involve interoperation among multiple components, ranging from dat…