no code implementations • EMNLP 2020 • Prithviraj Sen, Marina Danilevsky, Yunyao Li, Siddhartha Brahma, Matthias Boehm, Laura Chiticariu, Rajasekar Krishnamurthy
Our user studies confirm that the learned LEs are explainable and capture domain semantics.
1 code implementation • NAACL 2022 • Li Zhang, Ishan Jindal, Yunyao Li
Given a sentence and the predicate, a semantic role label is assigned to each argument of the predicate.
no code implementations • ACL (RepL4NLP) 2021 • Irene Li, Prithviraj Sen, Huaiyu Zhu, Yunyao Li, Dragomir Radev
In this paper, we propose zero-shot instance-weighting, a general model-agnostic zero-shot learning framework for improving CLTC by leveraging source instance weighting.
no code implementations • LREC 2022 • Ishan Jindal, Alexandre Rademaker, Michał Ulewicz, Ha Linh, Huyen Nguyen, Khoi-Nguyen Tran, Huaiyu Zhu, Yunyao Li
Semantic role labeling (SRL) represents the meaning of a sentence in the form of predicate-argument structures.
no code implementations • 23 May 2024 • Revanth Gangi Reddy, Omar Attia, Yunyao Li, Heng Ji, Saloni Potdar
Ranking is a fundamental and popular problem in search.
no code implementations • 2 Apr 2024 • Junxiong Wang, Ali Mousavi, Omar Attia, Ronak Pradeep, Saloni Potdar, Alexander M. Rush, Umar Farooq Minhas, Yunyao Li
Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark.
Ranked #1 on Entity Linking on KORE50
1 code implementation • 27 Nov 2023 • Simone Conia, Min Li, Daniel Lee, Umar Farooq Minhas, Ihab Ilyas, Yunyao Li
Recent work in Natural Language Processing and Computer Vision has been using textual information -- e. g., entity names and descriptions -- available in knowledge graphs to ground neural models to high-quality structured data.
no code implementations • 16 Nov 2023 • Jiaju Chen, Yuxuan Lu, Shao Zhang, Bingsheng Yao, Yuanzhe Dong, Ying Xu, Yunyao Li, Qianwen Wang, Dakuo Wang, Yuling Sun
AI models (including LLM) often rely on narrative question-answering (QA) datasets to provide customized QA functionalities to support downstream children education applications; however, existing datasets only include QA pairs that are grounded within the given storybook content, but children can learn more when teachers refer the storybook content to real-world knowledge (e. g., commonsense knowledge).
no code implementations • 26 Oct 2023 • Farima Fatahi Bayat, Kun Qian, Benjamin Han, Yisi Sang, Anton Belyi, Samira Khorshidi, Fei Wu, Ihab F. Ilyas, Yunyao Li
Detecting factual errors in textual information, whether generated by large language models (LLM) or curated by humans, is crucial for making informed decisions.
no code implementations • 20 Sep 2023 • Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theo Rekatsinas, Benjamin Han, Yunyao Li, Jeff Pound, Josh Susskind, Natalie Schluter, Ihab Ilyas, Navdeep Jaitly
Guided by these observations, we construct a new, improved dataset called LAGRANGE using heuristics meant to improve equivalence between KG and text and show the impact of each of the heuristics on cyclic evaluation.
1 code implementation • 22 May 2023 • Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang
Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations.
no code implementations • 16 May 2023 • Ihab F. Ilyas, JP Lacerda, Yunyao Li, Umar Farooq Minhas, Ali Mousavi, Jeffrey Pound, Theodoros Rekatsinas, Chiraag Sumanth
We then describe how our platform, including graph embeddings, can be leveraged to create a Semantic Annotation service that links unstructured Web documents to entities in our KG.
no code implementations • 15 Nov 2022 • Kevin Pei, Ishan Jindal, Kevin Chen-Chuan Chang, ChengXiang Zhai, Yunyao Li
Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks.
1 code implementation • 12 Oct 2022 • Ishan Jindal, Alexandre Rademaker, Khoi-Nguyen Tran, Huaiyu Zhu, Hiroshi Kanayama, Marina Danilevsky, Yunyao Li
In this paper, we address key practical issues with existing evaluation scripts and propose a more strict SRL evaluation metric PriMeSRL.
1 code implementation • 2 Aug 2022 • Eyal Shnarch, Alon Halfon, Ariel Gera, Marina Danilevsky, Yannis Katsis, Leshem Choshen, Martin Santillan Cooper, Dina Epelboim, Zheng Zhang, Dakuo Wang, Lucy Yip, Liat Ein-Dor, Lena Dankin, Ilya Shnayderman, Ranit Aharonov, Yunyao Li, Naftali Liberman, Philip Levin Slesarev, Gwilym Newton, Shila Ofek-Koifman, Noam Slonim, Yoav Katz
Text classification can be useful in many real-world scenarios, saving a lot of time for end users.
1 code implementation • Findings (ACL) 2022 • Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang, Yunyao Li, Lucian Popa, ChengXiang Zhai
We propose a probabilistic approach to select a subset of a \textit{target domain representative keywords} from a candidate set, contrasting with a context domain.
1 code implementation • ACL 2021 • Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray
Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversational systems.
no code implementations • NAACL 2021 • Arvind Agarwal, Laura Chiticariu, Poornima Chozhiyath Raman, Marina Danilevsky, Diman Ghazi, Ankush Gupta, Shanmukha Guttula, Yannis Katsis, Rajasekar Krishnamurthy, Yunyao Li, Shubham Mudgal, Vitobha Munigala, Nicholas Phan, Dhaval Sonawane, Sneha Srinivasan, Sudarshan R. Thitte, Mitesh Vasa, Ramiya Venkatachalam, Vinitha Yaski, Huaiyu Zhu
Contracts are arguably the most important type of business documents.
no code implementations • NAACL 2021 • Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li
Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i. e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems.
no code implementations • 16 Feb 2021 • Nancy Xin Ru Wang, Douglas Burdick, Yunyao Li
Perfect extraction quality is difficult to achieve with one single out-of-box model due to (1) the wide variety of table styles, (2) the lack of training data representing this variety and (3) the inherent ambiguity and subjectivity of table definitions between end-users.
1 code implementation • Findings (ACL) 2021 • Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramon Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
1 code implementation • COLING 2020 • Qiuhao Lu, Nisansa de Silva, Dejing Dou, Thien Huu Nguyen, Prithviraj Sen, Berthold Reinwald, Yunyao Li
Network representation learning (NRL) is crucial in the area of graph learning.
1 code implementation • 29 Nov 2020 • Ishan Jindal, Ranit Aharonov, Siddhartha Brahma, Huaiyu Zhu, Yunyao Li
Deep neural models achieve some of the best results for semantic role labeling.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Ishan Jindal, Yunyao Li, Siddhartha Brahma, Huaiyu Zhu
Although different languages have different argument annotations, polyglot training, the idea of training one model on multiple languages, has previously been shown to outperform monolingual baselines, especially for low resource languages.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Youxuan Jiang, Huaiyu Zhu, Jonathan K. Kummerfeld, Yunyao Li, Walter Lasecki
Resources for Semantic Role Labeling (SRL) are typically annotated by experts at great expense.
1 code implementation • EMNLP 2020 • Kun Qian, Poornima Chozhiyath Raman, Yunyao Li, Lucian Popa
Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation.
no code implementations • EMNLP 2020 • Li Zhang, Huaiyu Zhu, Siddhartha Brahma, Yunyao Li
Split and Rephrase is a text simplification task of rewriting a complex sentence into simpler ones.
no code implementations • ACL 2020 • Yunyao Li, Gr, Tyrone ison, Patricia Silveyra, Ali Douraghy, Xinyu Guan, Thomas Kieselbach, Chengkai Li, Haiqi Zhang
Just as SARS-CoV-2, a new form of coronavirus continues to infect a growing number of people around the world, harmful misinformation about the outbreak also continues to spread.
no code implementations • WS 2020 • Nikita Bhutani, Xinyi Zheng, Kun Qian, Yunyao Li, H. Jagadish
Knowledge-based question answering (KB{\_}QA) has long focused on simple questions that can be answered from a single knowledge source, a manually curated or an automatically extracted KB.
4 code implementations • ACL 2020 • Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Doug Burdick, Darrin Eide, Kathryn Funk, Yannis Katsis, Rodney Kinney, Yunyao Li, Ziyang Liu, William Merrill, Paul Mooney, Dewey Murdick, Devvret Rishi, Jerry Sheehan, Zhihong Shen, Brandon Stilson, Alex Wade, Kuansan Wang, Nancy Xin Ru Wang, Chris Wilhelm, Boya Xie, Douglas Raymond, Daniel S. Weld, Oren Etzioni, Sebastian Kohlmeier
The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research.
no code implementations • WS 2019 • Huaiyu Zhu, Yunyao Li, Laura Chiticariu
Natural language understanding at the semantic level and independent of language variations is of great practical value.
no code implementations • ACL 2019 • Yiwei Yang, Eser Kandogan, Yunyao Li, Walter S. Lasecki, Prithviraj Sen
While the role of humans is increasingly recognized in machine learning community, representation of and interaction with models in current human-in-the-loop machine learning (HITL-ML) approaches are too low-level and far-removed from human's conceptual models.
no code implementations • ACL 2019 • Jungo Kasai, Kun Qian, Sairam Gurajada, Yunyao Li, Lucian Popa
Recent adaptation of deep learning methods for ER mitigates the need for dataset-specific feature engineering by constructing distributed representations of entity records.
1 code implementation • CONLL 2018 • Min Li, Marina Danilevsky, Sara Noeman, Yunyao Li
Phonetic similarity algorithms identify words and phrases with similar pronunciation which are used in many natural language processing tasks.
no code implementations • COLING 2018 • Nikita Bhutani, Kun Qian, Yunyao Li, H. V. Jagadish, Hern, Mauricio ez, Mitesh Vasa
We show that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels.
no code implementations • NAACL 2018 • Laura Chiticariu, Marina Danilevsky, Yunyao Li, Frederick Reiss, Huaiyu Zhu
The rise of enterprise applications over unstructured and semi-structured documents poses new challenges to text understanding systems across multiple dimensions.
no code implementations • EMNLP 2017 • Chenguang Wang, Alan Akbik, Laura Chiticariu, Yunyao Li, Fei Xia, Anbang Xu
Crowdsourcing has proven to be an effective method for generating labeled data for a range of NLP tasks.
no code implementations • COLING 2016 • Alan Akbik, Laura Chiticariu, Marina Danilevsky, Yonas Kbrom, Yunyao Li, Huaiyu Zhu
We present PolyglotIE, a web-based tool for developing extractors that perform Information Extraction (IE) over multilingual data.
no code implementations • COLING 2016 • Alan Akbik, Xinyu Guan, Yunyao Li
To address these issues, we propose to manually alias TL verbs to existing English frames.
no code implementations • COLING 2016 • Alan Akbik, Yunyao Li
To overcome this challenge, we propose the use of instance-based learning that performs no explicit generalization, but rather extrapolates predictions from the most similar instances in the training data.