Huijia Zhu


TransAdv: A Translation-based Adversarial Learning Framework for Zero-Resource Cross-Lingual Named Entity Recognition
Yichun Zhao | Jintao Du | Gongshen Liu | Huijia Zhu
Findings of the Association for Computational Linguistics: EMNLP 2022

Zero-Resource Cross-Lingual Named Entity Recognition aims at training an NER model of the target language using only labeled source language data and unlabeled target language data. Existing methods are mainly divided into three categories: model transfer based, data transfer based and knowledge transfer based. Each method has its own disadvantages, and combining more than one of them often leads to better performance. However, the performance of data transfer based methods is often limited by inevitable noise in the translation process. To handle the problem, we propose a framework named TransAdv to mitigate lexical and syntactic errors of word-by-word translated data, better utilizing the data by multi-level adversarial learning and multi-model knowledge distillation. Extensive experiments are conducted over 6 target languages with English as the source language, and the results show that TransAdv achieves competitive performance to the state-of-the-art models.

A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction
Yichun Zhao | Kui Meng | Gongshen Liu | Jintao Du | Huijia Zhu
Proceedings of the 29th International Conference on Computational Linguistics

Aspect Sentiment Triplet Extraction (ASTE) aims at extracting triplets from a given sentence, where each triplet includes an aspect, its sentiment polarity, and a corresponding opinion explaining the polarity. Existing methods are poor at detecting complicated relations between aspects and opinions as well as classifying multiple sentiment polarities in a sentence. Detecting unclear boundaries of multi-word aspects and opinions is also a challenge. In this paper, we propose a Multi-Task Dual-Tree Network (MTDTN) to address these issues. We employ a constituency tree and a modified dependency tree in two sub-tasks of Aspect Opinion Co-Extraction (AOCE) and ASTE, respectively. To enhance the information interaction between the two sub-tasks, we further design a Transition-Based Inference Strategy (TBIS) that transfers the boundary information from tags of AOCE to ASTE through a transition matrix. Extensive experiments are conducted on four popular datasets, and the results show the effectiveness of our model.


Domain Adaptation with Latent Semantic Association for Named Entity Recognition
Honglei Guo | Huijia Zhu | Zhili Guo | Xiaoxun Zhang | Xian Wu | Zhong Su
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics


Dependency Parsing Based on Dynamic Local Optimization
Ting Liu | Jinshan Ma | Huijia Zhu | Sheng Li
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)