Jianhai Zhang


2022

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MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification
Jianhai Zhang | Mieradilijiang Maimaiti | Gao Xing | Yuanhang Zheng | Ji Zhang
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Text classification struggles to generalize to unseen classes with very few labeled text instances per class.In such a few-shot learning (FSL) setting, metric-based meta-learning approaches have shown promising results. Previous studies mainly aim to derive a prototype representation for each class.However, they neglect that it is challenging-yet-unnecessary to construct a compact representation which expresses the entire meaning for each class.They also ignore the importance to capture the inter-dependency between query and the support set for few-shot text classification. To deal with these issues, we propose a meta-learning based method MGIMN which performs instance-wise comparison followed by aggregation to generate class-wise matching vectors instead of prototype learning.The key of instance-wise comparison is the interactive matching within the class-specific context and episode-specific context. Extensive experiments demonstrate that the proposed method significantly outperforms the existing SOTA approaches, under both the standard FSL and generalized FSL settings.

2019

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Simple and Effective Text Matching with Richer Alignment Features
Runqi Yang | Jianhai Zhang | Xing Gao | Feng Ji | Haiqing Chen
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we present a fast and strong neural approach for general purpose text matching applications. We explore what is sufficient to build a fast and well-performed text matching model and propose to keep three key features available for inter-sequence alignment: original point-wise features, previous aligned features, and contextual features while simplifying all the remaining components. We conduct experiments on four well-studied benchmark datasets across tasks of natural language inference, paraphrase identification and answer selection. The performance of our model is on par with the state-of-the-art on all datasets with much fewer parameters and the inference speed is at least 6 times faster compared with similarly performed ones.

2016

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DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations
Honglei Li | Jianhai Zhang | Jian Wang | Hongfei Lin | Zhihao Yang
Proceedings of the 4th BioNLP Shared Task Workshop