王卓尔 Zhuoer Wang

Selected Publications

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 (Volume 1: Long Papers) Jul 2023

   Abs arXiv Bib PDF Code Poster Slides

   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-faithful,
     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 (Volume 1: Long Papers)},
     month = jul,
     year = {2023},
     address = {Toronto, Canada},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2023.acl-long.160},
     pages = {2847--2867},
     abbr = {ACL},
     code = {https://github.com/Edillower/CycleNLG},
     pdf = {Faithful_Low_Resource_Data_to_Text_Generation_through_Cycle_Training.pdf},
     arxiv = {2305.14793},
     slides = {ACL203-6min.pdf},
     poster = {ACL2023-poster.pdf},
     selected = {true},
     bibtex_show = {true}
   }

2. 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}
   }

3. 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 Poster

   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},
     poster = {AAAI2022-poster.pdf},
     bibtex_show = {true}
   }

4. 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}
   }