151 citations · 180 across the 5 of their papers we have counts for
12 papers
Overfitting or Underfitting? Understand Robustness Drop in Adversarial Training
Zichao Li, Liyuan Liu, Chengyu Dong +1
Our goal is to understand why the robustness drops after conducting adversarial training for too long. Although this phenomenon is commonly explained as overfitting, our analysis s…
Very Deep Transformers for Neural Machine Translation
Xiaodong Liu, Kevin Duh, Liyuan Liu +1
We explore the application of very deep Transformer models for Neural Machine Translation (NMT). Using a simple yet effective initialization technique that stabilizes training, we…
Partially-Typed NER Datasets Integration: Connecting Practice to Theory
Shi Zhi, Liyuan Liu, Yu Zhang +4
While typical named entity recognition (NER) models require the training set to be annotated with all target types, each available datasets may only cover a part of them. Instead o…
Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling
Ouyu Lan, Xiao Huang, Bill Yuchen Lin +3
Sequence labeling is a fundamental framework for various natural language processing problems. Its performance is largely influenced by the annotation quality and quantity in super…
CrossWeigh: Training Named Entity Tagger from Imperfect Annotations
Zihan Wang, Jingbo Shang, Liyuan Liu +3
Everyone makes mistakes. So do human annotators when curating labels for named entity recognition (NER). Such label mistakes might hurt model training and interfere model compariso…
Facet-Aware Evaluation for Extractive Summarization
Yuning Mao, Liyuan Liu, Qi Zhu +2
Commonly adopted metrics for extractive summarization focus on lexical overlap at the token level. In this paper, we present a facet-aware evaluation setup for better assessment of…