Publications

2023

1. ACL

   Faithful Low-resource Data-to-Text Generation through Cycle Training

   Zhuoer Wang, Marcus Collins, Nikhita Vedula, Simone Filice, Shervin Malmasi, and Oleg Rokhlenko

   In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL) Jul 2023

   Abs arXiv Bib PDF Code

   Methods to generate text from structured data have advanced significantly in recent years, primarily due to fine-tuning of pre-trained language models on large datasets. However, such models can fail to produce output faithful to the input data, particularly on out-of-domain data. Sufficient annotated data is often not available for specific domains, leading us to seek an unsupervised approach to improve the faithfulness of output text. Since the problem is fundamentally one of consistency between the representations of the structured data and text, we evaluate the effectiveness of cycle training in this work. Cycle training uses two models which are inverses of each other: one that generates text from structured data, and one which generates the structured data from natural language text. We show that cycle training, when initialized with a small amount of supervised data (100 samples in our case), achieves nearly the same performance as fully supervised approaches for the data-to-text generation task on the WebNLG, E2E, WTQ, and WSQL datasets. We perform extensive empirical analysis with automated evaluation metrics and a newly designed human evaluation schema to reveal different cycle training strategies’ effectiveness of reducing various types of generation errors. Our code is publicly available at https://github.com/Edillower/CycleNLG.

   @inproceedings{wang-etal-2023-cycle_d2t,
     title = {Faithful Low-resource Data-to-Text Generation through Cycle Training},
     author = {Wang, Zhuoer and Collins, Marcus and Vedula, Nikhita and Filice, Simone and Malmasi, Shervin and Rokhlenko, Oleg},
     booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL)},
     month = jul,
     year = {2023},
     address = {Toronto, Canada},
     publisher = {Association for Computational Linguistics},
     abbr = {ACL},
     arxiv = {2305.14793},
     code = {https://github.com/Edillower/CycleNLG},
     selected = {true},
     pdf = {Faithful_Low_Resource_Data_to_Text_Generation_through_Cycle_Training.pdf},
     bibtex_show = {true}
   }

2022

1. ALEXA

   Howdy Y’all: An Alexa TaskBot

   Texas A&M University

   In Alexa Prize TaskBot Challenge Proceedings 2022

   Abs Bib HTML PDF

   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},
     pdf = {howdy-yall-an-alexa-taskbot.pdf},
     bibtex_show = {true}
   }

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

   Abs Bib HTML PDF

   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}
   }

2021

1. arXiv

   Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models

   Jian Wu, Rajal Nivargi, Sree Sai Teja Lanka, Arjun Manoj Menon, Sai Ajay Modukuri, Nishanth Nakshatri, Xin Wei, Zhuoer Wang, James Caverlee, Sarah M Rajtmajer, and C. Lee Giles

   arXiv preprint arXiv:2104.04580 2021

   Abs arXiv Bib PDF

   In recent years, significant effort has been invested verifying the reproducibility and robustness of research claims in social and behavioral sciences (SBS), much of which has involved resource-intensive replication projects. In this paper, we investigate prediction of the reproducibility of SBS papers using machine learning methods based on a set of features. We propose a framework that extracts five types of features from scholarly work that can be used to support assessments of reproducibility of published research claims. Bibliometric features, venue features, and author features are collected from public APIs or extracted using open source machine learning libraries with customized parsers. Statistical features, such as p-values, are extracted by recognizing patterns in the body text. Semantic features, such as funding information, are obtained from public APIs or are extracted using natural language processing models. We analyze pairwise correlations between individual features and their importance for predicting a set of human-assessed ground truth labels. In doing so, we identify a subset of 9 top features that play relatively more important roles in predicting the reproducibility of SBS papers in our corpus. Results are verified by comparing performances of 10 supervised predictive classifiers trained on different sets of features.

   @article{wu2021predicting,
     title = {Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models},
     author = {Wu, Jian and Nivargi, Rajal and Lanka, Sree Sai Teja and Menon, Arjun Manoj and Modukuri, Sai Ajay and Nakshatri, Nishanth and Wei, Xin and Wang, Zhuoer and Caverlee, James and Rajtmajer, Sarah M and Giles, C. Lee},
     journal = {arXiv preprint arXiv:2104.04580},
     year = {2021},
     abbr = {arXiv},
     arxiv = {2104.04580},
     pdf = {2104.04580.pdf},
     bibtex_show = {true}
   }

2020

1. 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

   Abs Bib PDF Code

   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},
     pdf = {2020.emnlp-main.611.pdf},
     code = {https://github.com/heyunh2015/PARADE_dataset},
     selected = {true},
     bibtex_show = {true}
   }

2018

1. NT

   Fault propagation and effects analysis for designing an online monitoring system for the secondary loop of the nuclear power plant portion of a hybrid energy system

   Xiaoxu Diao, Yunfei Zhao, Mike Pietrykowski, Zhuoer Wang, Shannon Bragg-Sitton, and Carol Smidts

   Nuclear Technology 2018

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   This paper studies the propagation and effects of faults in critical components that pertain to the secondary loop of a nuclear power plant found in nuclear hybrid energy systems (NHESs). This information is used to design an online monitoring (OLM) system that is capable of detecting and analyzing faults that are likely to occur during NHES operation. In this research, the causes, features, and effects of possible faults are investigated by simulating the propagation of faults in the secondary loop of a nuclear power plant. The simulation is conducted using Integrated System Failure Analysis (ISFA), a promising method analyzing hardware and software faults during the conceptual design phase. First, the models of system components required by ISFA are constructed. Then, fault propagation analysis is implemented, which is conducted under the bounds set by acceptance criteria derived for the design of an OLM system. The result of the fault simulation is utilized to evaluate the effectiveness of signals for fault detection and diagnosis and to propose an optimization plan for the OLM system. Finally, several experiments are designed and conducted using a hardware-in-the-loop system to verify the correctness and effectiveness of the proposed method.

   @article{diao2018fault,
     title = {Fault propagation and effects analysis for designing an online monitoring system for the secondary loop of the nuclear power plant portion of a hybrid energy system},
     author = {Diao, Xiaoxu and Zhao, Yunfei and Pietrykowski, Mike and Wang, Zhuoer and Bragg-Sitton, Shannon and Smidts, Carol},
     journal = {Nuclear Technology},
     volume = {202},
     number = {2-3},
     pages = {106--123},
     year = {2018},
     publisher = {Taylor \& Francis},
     abbr = {NT},
     html = {https://www.tandfonline.com/doi/full/10.1080/00295450.2018.1426963},
     bibtex_show = {true}
   }