About Me
I am a third-year PhD student researcher focusing on Natural Language Processing, mentored by Prof. Chris Callison-Burch at the University of Pennsylvania. I was an undergraduate at the University of Michigan in 2018, previously mentored by Prof. Rada Mihalcea and Prof. Dragomir Radev. I'm also a drummer, musician, producer, and video creator.
CVUniversity of Pennsylvania
Shenzhen, China
zharry@seas.upenn.edu
Music
I play, record and produce music regularly. I enjoy genres ranging from progressive metal to pop to jazz. I'm passionate about rearranging and recreating soundtracks from video games and animes (ACG). I take pride in my 10k-subscriber channel on Bilibili. My music can be found on all major streaming platforms and YouTube.
ACG music rearranged and remade as metal or rock
ACG music rearranged and remade as jazz, fusion or funk
Metal music covers
Drum covers
Guitar and bass covers
This work is a part of the DARPA AIDA project. From the texts, audios and videos recounting the Russia-Ukraine conflict in 2014, the goal is to extract knowledge elements and generate hypotheses about real-life events. I used named entity recognition, keyword extraction and word embeddings to extract textual entities from the data and assign them with categories from the given ontology.
In each volume of the New Yorker magazine, there is a comic section where thousands of readers submit funny captions. The goal is to automatically divide them into clusters based on their theme of humor (what they are joking about) using unsupervised learning. Work had been done years ago but the codes were scattered and underdocumented. I as a freshman was in charge of this project, to bring the existing system up to date and to make optimization.
AAN encompases our corpus of resources on NLP and related fields and the research projects which build upon this corpus. We have collected around 6,500 surveys, tutorials and other resources and created a search engine which allows users to easily browse these resources. I helped build and maintain this power anthology with information regarding numerous papers included in top NLP venues. It features paper citation, author citation, and author collaboration, etc.
Paper BibTeX Repo In NAACL 2022.
@inproceedings{lyu-etal-2022-favorite, title = "Is 'my favorite new movie' my favorite movie? Probing the Understanding of Recursive Noun Phrases", author = "Lyu, Qing and Zheng, Hua and Li, Daoxin and Zhang, Li and Apidianaki, Marianna and Callison-Burch, Chris", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = july, year = "2022", address = "Seattle, USA", publisher = "Association for Computational Linguistics" }
@inproceedings{zhang-etal-2022-label, title = "Label Definitions Improve Semantic Role Labeling", author = "Zhang, Li and Jindal, Ishan and Li, Yunyao", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = july, year = "2022", address = "Seattle, USA", publisher = "Association for Computational Linguistics" }
Paper BibTeX Demo In ACL 2022.
@inproceedings{zhou-etal-2022-show, title = "Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data", author = "Zhou, Shuyan and Zhang, Li and Yang, Yue and Lyu, Qing and Yin, Pengcheng and Callison-Burch, Chris and Neubig, Graham", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.214", pages = "2998--3012", abstract = "Procedures are inherently hierarchical. To {``}make videos{''}, one may need to {``}purchase a camera{''}, which in turn may require one to {``}set a budget{''}. While such hierarchical knowledge is critical for reasoning about complex procedures, most existing work has treated procedures as shallow structures without modeling the parent-child relation. In this work, we attempt to construct an open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow, a website containing more than 110k instructional articles, each documenting the steps to carry out a complex procedure. To this end, we develop a simple and efficient method that links steps (e.g., {``}purchase a camera{''}) in an article to other articles with similar goals (e.g., {``}how to choose a camera{''}), recursively constructing the KB. Our method significantly outperforms several strong baselines according to automatic evaluation, human judgment, and application to downstream tasks such as instructional video retrieval.", }
Paper BibTeX In EMNLP 2021; presented at the 2nd Workshop on Advances in Language and Vision Research at NAACL 2021.
@inproceedings{yang-etal-2021-visual, title = "Visual Goal-Step Inference using wiki{H}ow", author = "Yang, Yue and Panagopoulou, Artemis and Lyu, Qing and Zhang, Li and Yatskar, Mark and Callison-Burch, Chris", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.165", pages = "2167--2179", abstract = "Understanding what sequence of steps are needed to complete a goal can help artificial intelligence systems reason about human activities. Past work in NLP has examined the task of goal-step inference for text. We introduce the visual analogue. We propose the Visual Goal-Step Inference (VGSI) task, where a model is given a textual goal and must choose which of four images represents a plausible step towards that goal. With a new dataset harvested from wikiHow consisting of 772,277 images representing human actions, we show that our task is challenging for state-of-the-art multimodal models. Moreover, the multimodal representation learned from our data can be effectively transferred to other datasets like HowTo100m, increasing the VGSI accuracy by 15 - 20{\%}. Our task will facilitate multimodal reasoning about procedural events.", }
Paper BibTeX Repo In INLG 2021.
@inproceedings{lyu-etal-2021-goal, title = "Goal-Oriented Script Construction", author = "Lyu, Qing and Zhang, Li and Callison-Burch, Chris", booktitle = "Proceedings of the 14th International Conference on Natural Language Generation", month = aug, year = "2021", address = "Aberdeen, Scotland, UK", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.inlg-1.19", pages = "184--200", abstract = "The knowledge of scripts, common chains of events in stereotypical scenarios, is a valuable asset for task-oriented natural language understanding systems. We propose the Goal-Oriented Script Construction task, where a model produces a sequence of steps to accomplish a given goal. We pilot our task on the first multilingual script learning dataset supporting 18 languages collected from wikiHow, a website containing half a million how-to articles. For baselines, we consider both a generation-based approach using a language model and a retrieval-based approach by first retrieving the relevant steps from a large candidate pool and then ordering them. We show that our task is practical, feasible but challenging for state-of-the-art Transformer models, and that our methods can be readily deployed for various other datasets and domains with decent zero-shot performance.", }
Paper BibTeX Repo In AACL-IJCNLP 2020.
@inproceedings{zhang-etal-2020-intent, title = "Intent Detection with {W}iki{H}ow", author = "Zhang, Li and Lyu, Qing and Callison-Burch, Chris", booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing", month = dec, year = "2020", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.aacl-main.35", pages = "328--333", abstract = "Modern task-oriented dialog systems need to reliably understand users{'} intents. Intent detection is even more challenging when moving to new domains or new languages, since there is little annotated data. To address this challenge, we present a suite of pretrained intent detection models which can predict a broad range of intended goals from many actions because they are trained on wikiHow, a comprehensive instructional website. Our models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog datasets. Our models also demonstrate strong zero- and few-shot performance, reaching over 75{\%} accuracy using only 100 training examples in all datasets.", }
Paper BibTeX Repo In EMNLP 2020; presented at Workshop on Enormous Language Models at ICLR 2021; a part of the Beyond the Imitation Game Benchmark.
@inproceedings{zhang-etal-2020-reasoning, title = "Reasoning about Goals, Steps, and Temporal Ordering with {W}iki{H}ow", author = "Zhang, Li and Lyu, Qing and Callison-Burch, Chris", 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://www.aclweb.org/anthology/2020.emnlp-main.374", pages = "4630--4639", }
Paper BibTeX Repo In EMNLP 2020; a part of the GEM Benchmark.
@inproceedings{zhang-etal-2020-small, title = "Small but Mighty: New Benchmarks for Split and Rephrase", author = "Zhang, Li and Zhu, Huaiyu and Brahma, Siddhartha and Li, Yunyao", 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://www.aclweb.org/anthology/2020.emnlp-main.91", pages = "1198--1205", }
Paper BibTeX Slides In *SEM 2019.
@inproceedings{zhang-etal-2019-multi, title = "Multi-Label Transfer Learning for Multi-Relational Semantic Similarity", author = "Zhang, Li and Wilson, Steven and Mihalcea, Rada", booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)", month = jun, year = "2019", address = "Minneapolis, Minnesota", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/S19-1005", pages = "44--50", abstract = "Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on. Yet, all the systems to date designed to capture such relations target one relation at a time. We propose a multi-label transfer learning approach based on LSTM to make predictions for several relations simultaneously and aggregate the losses to update the parameters. This multi-label regression approach jointly learns the information provided by the multiple relations, rather than treating them as separate tasks. Not only does this approach outperform the single-task approach and the traditional multi-task learning approach, but it also achieves state-of-the-art performance on all but one relation of the Human Activity Phrase dataset.", }
Paper BibTeX Poster In arXiv pre-print; presented at IC2S2 2018.
@misc{zhang2018direct, title={Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity}, author={Li Zhang and Steven R. Wilson and Rada Mihalcea}, year={2018}, eprint={1804.07835}, archivePrefix={arXiv}, primaryClass={cs.CL} }
Paper BibTeX Poster In TAC 2018.
@article{Burdick2018EntityAE, title={Entity and Event Extraction from Scratch Using Minimal Training Data}, author={Laura Burdick and Steven R. Wilson and Oana Ignat and Charles F Welch and Li Zhang and Mingzhe Wang and Jia Deng and Rada Mihalcea}, journal={Theory and Applications of Categories}, year={2018} }
Paper BibTeX Repo Poster In ACL 2018.
@InProceedings{acl18sql, author = {Catherine Finegan-Dollak\* and Jonathan K. Kummerfeld\* and Li Zhang and Karthik Ramanathan and Sesh Sadasivam and Rui Zhang and Dragomir Radev}, title = {Improving Text-to-SQL Evaluation Methodology}, booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, shortvenue = {ACL}, month = {July}, year = {2018}, address = {Melbourne, Victoria, Australia}, pages = {351--360}, abstract = {To be informative, an evaluation must measure how well systems generalize to realistic unseen data. We identify limitations of and propose improvements to current evaluations of text-to-SQL systems. First, we compare human-generated and automatically generated questions, characterizing properties of queries necessary for real-world applications. To facilitate evaluation on multiple datasets, we release standardized and improved versions of seven existing datasets and one new text-to-SQL dataset. Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work. Finally, we demonstrate how the common practice of anonymizing variables during evaluation removes an important challenge of the task. Our observations highlight key difficulties, and our methodology enables effective measurement of future development.}, url = {http://aclweb.org/anthology/P18-1033}, software = {https://github.com/jkkummerfeld/text2sql-data}, data = {https://github.com/jkkummerfeld/text2sql-data}, }
I did NLP research and software development on semantic role labeling, and previously, text simplification.
At Penn, I instructed CIS 530: Computational Linguistics (Winter, Fall 2020). At Michigan, I instructed EECS 595: Natural Language Processing (Fall 2018) and EECS 280: Programming and Introductory Data Structures (Winter, Fall 2016).
I performed software engineering, data analytics and machine learning.