1 code implementation • 21 Mar 2023 • Yushi Hu, Benlin Liu, Jungo Kasai, Yizhong Wang, Mari Ostendorf, Ranjay Krishna, Noah A. Smith
We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA).
no code implementations • 20 Dec 2022 • Hamish Ivison, Akshita Bhagia, Yizhong Wang, Hannaneh Hajishirzi, Matthew Peters
By converting instructions into modules, HINT models can effectively disregard the length of instructions and few-shot example inputs in terms of compute usage.
8 code implementations • 20 Dec 2022 • Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi
Applying our method to vanilla GPT3, we demonstrate a 33% absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT_001, which is trained with private user data and human annotations.
1 code implementation • 19 Dec 2022 • Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A. Smith, Luke Zettlemoyer, Tao Yu
Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets.
5 code implementations • 16 Apr 2022 • Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, Siddhartha Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi, Daniel Khashabi
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
1 code implementation • Findings (EMNLP) 2021 • Leo Z. Liu, Yizhong Wang, Jungo Kasai, Hannaneh Hajishirzi, Noah A. Smith
Models of language trained on very large corpora have been demonstrated useful for NLP.
no code implementations • ICLR 2021 • Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi, Jonathan Berant
When answering complex questions, people can seamlessly combine information from visual, textual and tabular sources.
1 code implementation • 23 Dec 2020 • Yue Guo, Wei Qiu, Yizhong Wang, Trevor Cohen
Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes.
2 code implementations • CCL 2020 • Qianying Liu, Sicong Jiang, Yizhong Wang, Sujian Li
In this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast.
6 code implementations • EMNLP 2020 • Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, Yejin Choi
Experiments across four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics.
1 code implementation • IJCNLP 2019 • Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, Matt Gardner
The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks.
no code implementations • 30 Jun 2019 • Xin Zhang, An Yang, Sujian Li, Yizhong Wang
Machine reading comprehension aims to teach machines to understand a text like a human and is a new challenging direction in Artificial Intelligence.
3 code implementations • NAACL 2019 • Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner
We introduce a new English reading comprehension benchmark, DROP, which requires Discrete Reasoning Over the content of Paragraphs.
Ranked #12 on Question Answering on DROP Test
1 code implementation • EMNLP 2018 • Yizhong Wang, Sujian Li, Jingfeng Yang
Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis.
1 code implementation • ACL 2018 • Shuming Ma, Xu sun, Yizhong Wang, Junyang Lin
However, most of the existing neural machine translation models only use one of the correct translations as the targets, and the other correct sentences are punished as the incorrect sentences in the training stage.
no code implementations • ACL 2018 • Yizhong Wang, Kai Liu, Jing Liu, wei he, Yajuan Lyu, Hua Wu, Sujian Li, Haifeng Wang
Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine.
Ranked #3 on Question Answering on MS MARCO
no code implementations • IJCNLP 2017 • Yizhong Wang, Sujian Li, Jingfeng Yang, Xu sun, Houfeng Wang
Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text.
General Classification Implicit Discourse Relation Classification +2
3 code implementations • WS 2018 • Wei He, Kai Liu, Jing Liu, Yajuan Lyu, Shiqi Zhao, Xinyan Xiao, Yu-An Liu, Yizhong Wang, Hua Wu, Qiaoqiao She, Xuan Liu, Tian Wu, Haifeng Wang
Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements.
1 code implementation • ACL 2017 • Yizhong Wang, Sujian Li, Houfeng Wang
Previous work introduced transition-based algorithms to form a unified architecture of parsing rhetorical structures (including span, nuclearity and relation), but did not achieve satisfactory performance.
Ranked #5 on Discourse Parsing on RST-DT
no code implementations • COLING 2016 • Wenjing Fang, Kenny Zhu, Yizhong Wang, Jia Tan
This paper presents a novel high-order dependency parsing framework that targets non-projective treebanks.