王卓尔 Zhuoer Wang

Selected Publications

1. ALEXA

   Howdy Y’all: An Alexa TaskBot

   Texas A&M University

   In Alexa Prize TaskBot Challenge Proceedings 2022

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   In this paper, we present Howdy Y’all, a multi-modal task-oriented dialogue agent developed for the 2021-2022 Alexa Prize TaskBot competition. Our design principles guiding Howdy Y’all aim for high user satisfaction through friendly and trustworthy encounters, minimization of negative conversation edge cases, and wide coverage over many tasks. Hence, Howdy Y’all is built upon a rapid prototyping platform to enable fast experimentation and powered by four key innovations to enable this vision: (i) First, it combines a rules, phonetic matching, and a transformer-based approach for robust intent understanding. (ii) Second, to accurately elicit user preferences and guide users to the right task, Howdy Y’all is powered by a contrastive learning search framework over sentence embeddings and a conversational recommender for eliciting preferences. (iii) Third, to support a variety of user question types, it introduces a new data augmentation method for question generation and a self-supervised answer selection approach for improving question answering. (iv) Finally, to help motivate our users and keep them engaged, we design an emotional conversation tracker that provides empathetic responses to keep users engaged and a monitor of conversation quality.

   @inproceedings{howdyyall-2022,
     author = {University, Texas A&M},
     title = {Howdy Y’all: An Alexa TaskBot},
     year = {2022},
     html = {https://www.amazon.science/alexa-prize/proceedings/howdy-yall-an-alexa-taskbot},
     booktitle = {Alexa Prize TaskBot Challenge Proceedings},
     selected = {true},
     abbr = {ALEXA},
     bibtex_show = {true},
     pdf = {howdy-yall-an-alexa-taskbot.pdf}
   }

2. AAAI

   RES: An Interpretable Replicability Estimation System for Research Publications

   Zhuoer Wang, Qizhang Feng, Mohinish Chatterjee, Xing Zhao, Yezi Liu, Yuening Li, Abhay Kumar Singh, Frank M. Shipman, Xia Hu, and James Caverlee

   In AAAI 2022

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   Reliable and faithful research is the cornerstone of breakthrough advancements and disruptive innovations. Assessing the credibility of scientific findings and claims in research publications has long been a time-consuming and challenging task for researchers and decision-makers. In this paper, we introduce RES - an intelligent system that assists humans in analyzing the credibility of scientific findings and claims in research publications in the field of social and behavioral sciences by estimating their replicability. The pipeline of RES consists of four major modules that perform feature extraction, replicability estimation, result explanation, and sentiment analysis respectively. Our evaluation based on human experts’ assessments suggests that the RES has achieved adequate performance. The RES is also built with a Graphical User Interface (GUI) that is publicly accessible at https://tamu-infolab.github.io/RES

   @inproceedings{wang-etal-2022-res,
     title = {RES: An Interpretable Replicability Estimation System for Research Publications},
     author = {Wang, Zhuoer and Feng, Qizhang and Chatterjee, Mohinish and Zhao, Xing and Liu, Yezi and Li, Yuening and Singh, Abhay Kumar and Shipman, Frank M. and Hu, Xia and Caverlee, James},
     booktitle = {AAAI},
     year = {2022},
     abbr = {AAAI},
     html = {https://tamu-infolab.github.io/RES/},
     selected = {true},
     pdf = {21737-Article Text-25750-1-2-20220628.pdf},
     bibtex_show = {true}
   }

3. EMNLP

   PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge

   Yun He, Zhuoer Wang, Yin Zhang, Ruihong Huang, and James Caverlee

   In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Nov 2020

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   We present a new benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge. PARADE contains paraphrases that overlap very little at the lexical and syntactic level but are semantically equivalent based on computer science domain knowledge, as well as non-paraphrases that overlap greatly at the lexical and syntactic level but are not semantically equivalent based on this domain knowledge. Experiments show that both state-of-the-art neural models and non-expert human annotators have poor performance on PARADE. For example, BERT after fine-tuning achieves an F1 score of 0.709, which is much lower than its performance on other paraphrase identification datasets. PARADE can serve as a resource for researchers interested in testing models that incorporate domain knowledge. We make our data and code freely available.

   @inproceedings{he-etal-2020-parade,
     title = {{PARADE}: {A} {N}ew {D}ataset for {P}araphrase {I}dentification {R}equiring {C}omputer {S}cience {D}omain {K}nowledge},
     author = {He, Yun and Wang, Zhuoer and Zhang, Yin and Huang, Ruihong and Caverlee, James},
     booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
     month = nov,
     year = {2020},
     address = {Online},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2020.emnlp-main.611},
     doi = {10.18653/v1/2020.emnlp-main.611},
     pages = {7572--7582},
     abbr = {EMNLP},
     bibtex_show = {true},
     pdf = {2020.emnlp-main.611.pdf},
     code = {https://github.com/heyunh2015/PARADE_dataset},
     selected = {true}
   }