Yiyuan Li


2022

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SPE: Symmetrical Prompt Enhancement for Fact Probing
Yiyuan Li | Tong Che | Yezhen Wang | Zhengbao Jiang | Caiming Xiong | Snigdha Chaturvedi
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Pretrained language models (PLMs) have been shown to accumulate factual knowledge during pretraining (Petroni et al. 2019). Recent works probe PLMs for the extent of this knowledge through prompts either in discrete or continuous forms. However, these methods do not consider symmetry of the task: object prediction and subject prediction. In this work, we propose Symmetrical Prompt Enhancement (SPE), a continuous prompt-based method for factual probing in PLMs that leverages the symmetry of the task by constructing symmetrical prompts for subject and object prediction. Our results on a popular factual probing dataset, LAMA, show significant improvement of SPE over previous probing methods.

2021

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Adversarial Scrubbing of Demographic Information for Text Classification
Somnath Basu Roy Chowdhury | Sayan Ghosh | Yiyuan Li | Junier Oliva | Shashank Srivastava | Snigdha Chaturvedi
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task. We aim to scrub such undesirable attributes and learn fair representations while maintaining performance on the target task. In this paper, we present an adversarial learning framework “Adversarial Scrubber” (AdS), to debias contextual representations. We perform theoretical analysis to show that our framework converges without leaking demographic information under certain conditions. We extend previous evaluation techniques by evaluating debiasing performance using Minimum Description Length (MDL) probing. Experimental evaluations on 8 datasets show that AdS generates representations with minimal information about demographic attributes while being maximally informative about the target task.

2020

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A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
Graham Neubig | Shruti Rijhwani | Alexis Palmer | Jordan MacKenzie | Hilaria Cruz | Xinjian Li | Matthew Lee | Aditi Chaudhary | Luke Gessler | Steven Abney | Shirley Anugrah Hayati | Antonios Anastasopoulos | Olga Zamaraeva | Emily Prud’hommeaux | Jennette Child | Sara Child | Rebecca Knowles | Sarah Moeller | Jeffrey Micher | Yiyuan Li | Sydney Zink | Mengzhou Xia | Roshan S Sharma | Patrick Littell
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited. In August 2019, a workshop was held at Carnegie Mellon University in Pittsburgh, PA, USA to attempt to bring together language community members, documentary linguists, and technologists to discuss how to bridge this gap and create prototypes of novel and practical language revitalization technologies. The workshop focused on developing technologies to aid language documentation and revitalization in four areas: 1) spoken language (speech transcription, phone to orthography decoding, text-to-speech and text-speech forced alignment), 2) dictionary extraction and management, 3) search tools for corpora, and 4) social media (language learning bots and social media analysis). This paper reports the results of this workshop, including issues discussed, and various conceived and implemented technologies for nine languages: Arapaho, Cayuga, Inuktitut, Irish Gaelic, Kidaw’ida, Kwak’wala, Ojibwe, San Juan Quiahije Chatino, and Seneca.