Search Results for author: Shuguang Chen

Found 8 papers, 4 papers with code

Context-aware Adversarial Attack on Named Entity Recognition

no code implementations16 Sep 2023 Shuguang Chen, Leonardo Neves, Thamar Solorio

In recent years, large pre-trained language models (PLMs) have achieved remarkable performance on many natural language processing benchmarks.

Adversarial Attack named-entity-recognition +1

Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach

no code implementations8 Jan 2023 Shuai Wang, ChiYung Yam, Shuguang Chen, Lihong Hu, Liping Li, Faan-Fung Hung, Jiaqi Fan, Chi-Ming Che, Guanhua Chen

Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters based on the combination of first-principles quantum mechanical method, machine learning (ML) and experimental calibration.

Ensemble Learning

Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition

1 code implementation14 Oct 2022 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 diversity of training data in low-resource scenarios.

Data Augmentation named-entity-recognition +4

CALCS 2021 Shared Task: Machine Translation for Code-Switched Data

no code implementations19 Feb 2022 Shuguang Chen, Gustavo Aguilar, Anirudh Srinivasan, Mona Diab, Thamar Solorio

For the unsupervised setting, we provide the following language pairs: English and Spanish-English (Eng-Spanglish), and English and Modern Standard Arabic-Egyptian Arabic (Eng-MSAEA) in both directions.

Language Identification Machine Translation +3

A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context

no code implementations NAACL (ACL) 2022 Man Luo, Shuguang Chen, Chitta Baral

Furthermore, we propose consistency and similarity constraints to promote the correlation and interaction between passage ranking and sentence selection. The experiments demonstrate that our framework can achieve competitive results with previous systems and outperform the baseline by 28\% in terms of exact matching of relevant sentences on the HotpotQA dataset.

Passage Ranking Question Answering +1

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