150 citations · 314 across the 17 of their papers we have counts for
28 papers · 1 filter
Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition
Shuguang Chen, Leonardo Neves, Thamar Solorio
In this work, we take the named entity recognition task in the English language as a case study and explore style transfer as a data augmentation method to increase the size and di…
CALCS 2021 Shared Task: Machine Translation for Code-Switched Data
Shuguang Chen, Gustavo Aguilar, Anirudh Srinivasan +2
To date, efforts in the code-switching literature have focused for the most part on language identification, POS, NER, and syntactic parsing. In this paper, we address machine tran…
From None to Severe: Predicting Severity in Movie Scripts
Yigeng Zhang, Mahsa Shafaei, Fabio Gonzalez +1
In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script. We first investigate categorizing the or…
Data Augmentation for Cross-Domain Named Entity Recognition
Shuguang Chen, Gustavo Aguilar, Leonardo Neves +1
Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-doma…
Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crisp
Shuguang Chen, Leonardo Neves, Thamar Solorio
Performance of neural models for named entity recognition degrades over time, becoming stale. This degradation is due to temporal drift, the change in our target variables' statist…
Learning to Emphasize: Dataset and Shared Task Models for Selecting Emphasis in Presentation Slides
Amirreza Shirani, Giai Tran, Hieu Trinh +5
Presentation slides have become a common addition to the teaching material. Emphasizing strong leading words in presentation slides can allow the audience to direct the eye to cert…