Yiwen Zhang


2022

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Learn to Adapt for Generalized Zero-Shot Text Classification
Yiwen Zhang | Caixia Yuan | Xiaojie Wang | Ziwei Bai | Yongbin Liu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Most existing methods generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes, and the parameters keep stationary in predicting procedures. To address these challenges, we propose a novel Learn to Adapt (LTA) network using a variant meta-learning framework. Specifically, LTA trains an adaptive classifier by using both seen and virtual unseen classes to simulate a generalized zero-shot learning (GZSL) scenario in accordance with the test time, and simultaneously learns to calibrate the class prototypes and sample representations to make the learned parameters adaptive to incoming unseen classes. We claim that the proposed model is capable of representing all prototypes and samples from both classes to a more consistent distribution in a global space. Extensive experiments on five text classification datasets show that our model outperforms several competitive previous approaches by large margins. The code and the whole datasets are available at https://github.com/Quareia/LTA.

2021

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Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference
Hai Hu | He Zhou | Zuoyu Tian | Yiwen Zhang | Yina Patterson | Yanting Li | Yixin Nie | Kyle Richardson
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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CLUE: A Chinese Language Understanding Evaluation Benchmark
Liang Xu | Hai Hu | Xuanwei Zhang | Lu Li | Chenjie Cao | Yudong Li | Yechen Xu | Kai Sun | Dian Yu | Cong Yu | Yin Tian | Qianqian Dong | Weitang Liu | Bo Shi | Yiming Cui | Junyi Li | Jun Zeng | Rongzhao Wang | Weijian Xie | Yanting Li | Yina Patterson | Zuoyu Tian | Yiwen Zhang | He Zhou | Shaoweihua Liu | Zhe Zhao | Qipeng Zhao | Cong Yue | Xinrui Zhang | Zhengliang Yang | Kyle Richardson | Zhenzhong Lan
Proceedings of the 28th International Conference on Computational Linguistics

The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP). The problem, however, is that most such benchmarks are limited to English, which has made it difficult to replicate many of the successes in English NLU for other languages. To help remedy this issue, we introduce the first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark. CLUE is an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text. To establish results on these tasks, we report scores using an exhaustive set of current state-of-the-art pre-trained Chinese models (9 in total). We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on Chinese NLU. Our benchmark is released at https://www.cluebenchmarks.com

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Building a Treebank for Chinese Literature for Translation Studies
Hai Hu | Yanting Li | Yina Patterson | Zuoyu Tian | Yiwen Zhang | He Zhou | Sandra Kuebler | Chien-Jer Charles Lin
Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories

2019

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Ensemble Methods to Distinguish Mainland and Taiwan Chinese
Hai Hu | Wen Li | He Zhou | Zuoyu Tian | Yiwen Zhang | Liang Zou
Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects

This paper describes the IUCL system at VarDial 2019 evaluation campaign for the task of discriminating between Mainland and Taiwan variation of mandarin Chinese. We first build several base classifiers, including a Naive Bayes classifier with word n-gram as features, SVMs with both character and syntactic features, and neural networks with pre-trained character/word embeddings. Then we adopt ensemble methods to combine output from base classifiers to make final predictions. Our ensemble models achieve the highest F1 score (0.893) in simplified Chinese track and the second highest (0.901) in traditional Chinese track. Our results demonstrate the effectiveness and robustness of the ensemble methods.