My research lies at the intersections of machine learning, natural language processing, and social media. I focus on designing algorithms for learning semantics from large data for natural language understanding, and natural language generation in particular with stylistic variations. I recently received the NSF CRII Award, NSF CAREER Award, Criteo Faculty Research Award, CrowdFlower AI for Everyone Award, Best Paper Award at COLING'18, as well as research funds from DARPA and IARPA. I was a postdoctoral researcher at the University of Pennsylvania. I received my PhD in Computer Science from New York University, MS and BS from Tsinghua University. I am also a faculty member of the Machine Learning Center and NSF AI CARING Institute at Georgia Tech.
I am a senior area chair for EMNLP 2022 (generation), NAACL 2022 (machine learning for NLP), 2021 (generation), and ACL 2020 (generation), and an area chair for EMNLP 2021 (computational social science), EMNLP 2020 (generation), AAAI 2020 (NLP), ACL 2019 (semantics), NAACL 2019 (generation), EMNLP 2018 (social media), COLING 2018 (semantics), EMNLP 2016 (generation), a workshop chair for ACL 2017, and the publicity chair for EMNLP 2019, NAACL 2018 and 2016. I also created a new undergraduate course on Social Media and Text Analytics.
I'm recruiting 1-2 new PhD students this year (apply to PhD program and list me as a potential advisor). I also advise undergraduate and MS students with research thesis option (apply to MSCS; email me).
Spring 2021, work with my phd student Mounica Maddela, Sebastian Gehrmann (Google Research), Dhruv Kumar (Borealis AI), and others on the 💎GEM Benchmark for natural language generation, evaluation, and metrics.
Apr 29, invited talk at NYU NLP/Text-as-Data Speaker Series
April 2020, three long papers accepted to ACL 2020! We are releasing (1) new high-quality dataset and Transformer-based model for text simplification; (2) fine-grained named entity and code recognition for StackOverflow; (3) a unified span-based neural network framework and benchmark leaderboard for 10+ NLP tasks.
Many text-to-text generation problems can be thought of as sentential paraphrasing or monolingual machine translation. It faces an exponential search space larger than bilingual translation, but a much smaller optimal solution space due to specific task requirements. I am interested in a variety of generation problems, including style transfer [COLING'12] and stylistics in general (e.g., historic ↔ modern, non-standard ↔ standard [BUCC'13], feminine ↔ masculine [AAAI'16]). Our latest work focuses on controllable text generation [NAACL'21]. My work uncovered multiple serious problems in previous research (from 2010 to 2014) on text simplification [TACL'15] , designed a new tunable metric SARI [TACL'16] which is effective for evaluation and as a learning objective for training (now added to TensorFlow by the Google AI group), optimized syntax-based machine translation models [TACL'16], created pairwise neural ranking models to for lexical simplification [EMNLP'18], and studied document-level simplification [AAAI'20]. Our newest Transformer-based model initialized with BERT is the current state-of-the-art for automatic text simplification [ACL'20a].
Natural Language Understanding / Semantics
My approach to natural language understanding is learning and modeling paraphrases on a much larger scale and with a much broader range than previous work, essentially by developing more robust machine learning models and leveraging social media data. These paraphrase can enable natural language systems to handle errors (e.g., “everytime” ↔ “every time”), lexical variations (e.g., “oscar nom’d doc” ↔ “Oscar-nominated documentary”), rare words (e.g “NetsBulls series” ↔ “Nets and Bulls games”), and language shifts (e.g. “is bananas” ↔ “is great”). We designed a series of unsupervised and supervised learning approaches for paraphrase identification in social media data (also applicable to question/answer pairs for QA systems), ranging from neural network models [COLING'18][NAACL'18a] to multi-instance learning [TACL'14][EMNLP'16], and crowdsourcing large-scale datasets [SemEval'15][EMNLP'17].
Noisy User-generated Data / Social Media
For AI to truly understand human language and help people (e.g., instructing a robot), we ought to study the language people actually use in their daily life (e.g., posting on social media), besides the formally written texts that are well supported by existing NLP software. I thus focus on specially designed learning algorithms and the data for training these algorithms to develop tools to process and analyze noisy user-generated data. I have worked a lot with Twitter data [EMNLP'19][EMNLP'17][EMNLP'16][TACL'14], given its importance and large scale coverage. Social media also contains very diverse languages for studying stylistics and semantics, carrying information that is important for both people’s everyday lives and national security. In the past three years, with my students, I have expanded my scope to cover a wider range of user-generated data, including biology lab protocols [NAACL'18b], GitHub, and StackOverflow [ACL'20b].
Current Students: Mounica Maddela (PhD student; controllable text generation/text simplification/ranking model) Chao Jiang (PhD student; semantics/word alignment/sentence alignment) Yao Dou (PhD student; generation/paraphrase) Tarek Naous (PhD student; multilingual NLP) Duong Minh Le
(PhD student; generation) Mohit Chandra (PhD student; NLP for social good) Yang Chen (PhD student; cross-lingual transfer/information extraction -- co-advisor: Alan Ritter) Junmo Kang (PhD student -- co-advisor: Alan Ritter) Jonathan Zheng (Undergrad, autumn 2020 -- ; robustness/social media) David Heineman (Undergrad, winter 2020 -- ; user interface/generation) Michael Ryan (Undergrad, winter 2020 -- ; text simplification) Vishnu Suresh (Undergrad, autumn 2021 -- ; medical NLP) Marcus Ma (Undergrad, spring 2022 -- ; stylistics) Elizabeth Liu (Undergrad, spring 2022 -- ) Vishnesh Jayanthi (Undergrad, summer 2022 -- ) Rachel Choi (Undergrad, summer 2022 -- ) Ian Ligon (Undergrad, summer 2022 -- ) Anton Lavrouk (Undergrad, autumn 2022 -- ) Kelly Smith (Undergrad, autumn 2022 -- ) Vinayak Athavale (Undergrad, autumn 2022 -- ) Chase Perry (MS student, summer 2022 -- ) Srushti Nandu (Research intern, summer 2021 -- ; social media)
PhD Thesis Committee: Sarah Wiegreffe (PhD @GaTech, 2022; interpretability/explainable AI - advisor: Mark Riedl) Yuval Pinter (PhD @GaTech, 2021; interpretability/semantics/morphology - advisor: Jacob Eisenstein) Sanqiang Zhao (PhD @UPitt, 2021; text simplification - advisor: Daqing He) Kai Cao (PhD @NYU, 2017; information extraction - advisor: Ralph Grishman) Maria Pershina (PhD @NYU, 2014; information extraction ACL'14 - advisor: Ralph Grishman)
