Yue Zhou


2022

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SPDB Innovation Lab at SemEval-2022 Task 3: Recognize Appropriate Taxonomic Relations Between Two Nominal Arguments with ERNIE-M Model
Yue Zhou | Bowei Wei | Jianyu Liu | Yang Yang
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Synonym and antonym practice are the most common practices in our early childhood. It correlated our known words to a better place deep in our intuition. At the beginning of life for a machine, we would like to treat the machine as a baby and built a similar training for it as well to present a qualified performance. In this paper, we present an ensemble model for sentence logistics classification, which outperforms the state-of-art methods. Our approach essentially builds on two models including ERNIE-M and DeBERTaV3. With cross validation and random seeds tuning, we select the top performance models for the last soft ensemble and make them vote for the final answer, achieving the top 6 performance.

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X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible Clarifications
Junyuan Shang | Shuohuan Wang | Yu Sun | Yanjun Yu | Yue Zhou | Li Xiang | Guixiu Yang
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper describes our winning system on SemEval 2022 Task 7: Identifying Plausible Clarifications ofImplicit and Underspecified Phrases in Instructional Texts. A replaced token detection pre-trained model is utilized with minorly different task-specific heads for SubTask-A: Multi-class Classification and SubTask-B: Ranking. Incorporating a pattern-aware ensemble method, our system achieves a 68.90% accuracy score and 0.8070 spearman’s rank correlation score surpassing the 2nd place with a large margin by 2.7 and 2.2 percent points for SubTask-A and SubTask-B, respectively. Our approach is simple and easy to implement, and we conducted ablation studies and qualitative and quantitative analyses for the working strategies used in our system.

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Towards Enhancing Health Coaching Dialogue in Low-Resource Settings
Yue Zhou | Barbara Di Eugenio | Brian Ziebart | Lisa Sharp | Bing Liu | Ben Gerber | Nikolaos Agadakos | Shweta Yadav
Proceedings of the 29th International Conference on Computational Linguistics

Health coaching helps patients identify and accomplish lifestyle-related goals, effectively improving the control of chronic diseases and mitigating mental health conditions. However, health coaching is cost-prohibitive due to its highly personalized and labor-intensive nature. In this paper, we propose to build a dialogue system that converses with the patients, helps them create and accomplish specific goals, and can address their emotions with empathy. However, building such a system is challenging since real-world health coaching datasets are limited and empathy is subtle. Thus, we propose a modularized health coaching dialogue with simplified NLU and NLG frameworks combined with mechanism-conditioned empathetic response generation. Through automatic and human evaluation, we show that our system generates more empathetic, fluent, and coherent responses and outperforms the state-of-the-art in NLU tasks while requiring less annotation. We view our approach as a key step towards building automated and more accessible health coaching systems.

2016

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Learning to Answer Biomedical Questions: OAQA at BioASQ 4B
Zi Yang | Yue Zhou | Eric Nyberg
Proceedings of the Fourth BioASQ workshop