Jinpeng Hu


2022

pdf bib
Graph Enhanced Contrastive Learning for Radiology Findings Summarization
Jinpeng Hu | Zhuo Li | Zhihong Chen | Zhen Li | Xiang Wan | Tsung-Hui Chang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The impression section of a radiology report summarizes the most prominent observation from the findings section and is the most important section for radiologists to communicate to physicians. Summarizing findings is time-consuming and can be prone to error for inexperienced radiologists, and thus automatic impression generation has attracted substantial attention. With the encoder-decoder framework, most previous studies explore incorporating extra knowledge (e.g., static pre-defined clinical ontologies or extra background information). Yet, they encode such knowledge by a separate encoder to treat it as an extra input to their models, which is limited in leveraging their relations with the original findings. To address the limitation, we propose a unified framework for exploiting both extra knowledge and the original findings in an integrated way so that the critical information (i.e., key words and their relations) can be extracted in an appropriate way to facilitate impression generation. In detail, for each input findings, it is encoded by a text encoder and a graph is constructed through its entities and dependency tree. Then, a graph encoder (e.g., graph neural networks (GNNs)) is adopted to model relation information in the constructed graph. Finally, to emphasize the key words in the findings, contrastive learning is introduced to map positive samples (constructed by masking non-key words) closer and push apart negative ones (constructed by masking key words). The experimental results on two datasets, OpenI and MIMIC-CXR, confirm the effectiveness of our proposed method, where the state-of-the-art results are achieved.

pdf bib
Hero-Gang Neural Model For Named Entity Recognition
Jinpeng Hu | Yaling Shen | Yang Liu | Xiang Wan | Tsung-Hui Chang
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Named entity recognition (NER) is a fundamental and important task in NLP, aiming at identifying named entities (NEs) from free text. Recently, since the multi-head attention mechanism applied in the Transformer model can effectively capture longer contextual information, Transformer-based models have become the mainstream methods and have achieved significant performance in this task. Unfortunately, although these models can capture effective global context information, they are still limited in the local feature and position information extraction, which is critical in NER. In this paper, to address this limitation, we propose a novel Hero-Gang Neural structure (HGN), including the Hero and Gang module, to leverage both global and local information to promote NER. Specifically, the Hero module is composed of a Transformer-based encoder to maintain the advantage of the self-attention mechanism, and the Gang module utilizes a multi-window recurrent module to extract local features and position information under the guidance of the Hero module. Afterward, the proposed multi-window attention effectively combines global information and multiple local features for predicting entity labels. Experimental results on several benchmark datasets demonstrate the effectiveness of our proposed model.

pdf bib
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction
Yang Liu | Jinpeng Hu | Xiang Wan | Tsung-Hui Chang
Findings of the Association for Computational Linguistics: ACL 2022

Few-Shot Relation Extraction aims at predicting the relation for a pair of entities in a sentence by training with a few labelled examples in each relation. Some recent works have introduced relation information (i.e., relation labels or descriptions) to assist model learning based on Prototype Network. However, most of them constrain the prototypes of each relation class implicitly with relation information, generally through designing complex network structures, like generating hybrid features, combining with contrastive learning or attention networks. We argue that relation information can be introduced more explicitly and effectively into the model. Thus, this paper proposes a direct addition approach to introduce relation information. Specifically, for each relation class, the relation representation is first generated by concatenating two views of relations (i.e., [CLS] token embedding and the mean value of embeddings of all tokens) and then directly added to the original prototype for both train and prediction. Experimental results on the benchmark dataset FewRel 1.0 show significant improvements and achieve comparable results to the state-of-the-art, which demonstrates the effectiveness of our proposed approach. Besides, further analyses verify that the direct addition is a much more effective way to integrate the relation representations and the original prototypes.

pdf bib
Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction
Yang Liu | Jinpeng Hu | Xiang Wan | Tsung-Hui Chang
Findings of the Association for Computational Linguistics: NAACL 2022

Few-shot Relation Extraction refers to fast adaptation to novel relation classes with few samples through training on the known relation classes. Most existing methods focus on implicitly introducing relation information (i.e., relation label or relation description) to constrain the prototype representation learning, such as contrastive learning, graphs, and specifically designed attentions, which may bring useless and even harmful parameters. Besides, these approaches are limited in handing outlier samples far away from the class center due to the weakly implicit constraint. In this paper, we propose an effective and parameter-less Prototype Rectification Method (PRM) to promote few-shot relation extraction, where we utilize a prototype rectification module to rectify original prototypes explicitly by the relation information. Specifically, PRM is composed of two gate mechanisms. One gate decides how much of the original prototype remains, and another one updates the remained prototype with relation information. In doing so, better and stabler global relation information can be captured for guiding prototype representations, and thus PRM can robustly deal with outliers. Moreover, we also extend PRM to both none-of-the-above (NOTA) and domain adaptation scenarios. Experimental results on FewRel 1.0 and 2.0 datasets demonstrate the effectiveness of our proposed method, which achieves state-of-the-art performance.

pdf bib
A Label-Aware Autoregressive Framework for Cross-Domain NER
Jinpeng Hu | He Zhao | Dan Guo | Xiang Wan | Tsung-Hui Chang
Findings of the Association for Computational Linguistics: NAACL 2022

Cross-domain named entity recognition (NER) aims to borrow the entity information from the source domain to help the entity recognition in the target domain with limited labeled data. Despite the promising performance of existing approaches, most of them focus on reducing the discrepancy of token representation between source and target domains, while the transfer of the valuable label information is often not explicitly considered or even ignored. Therefore, we propose a novel autoregressive framework to advance cross-domain NER by first enhancing the relationship between labels and tokens and then further improving the transferability of label information. Specifically, we associate each label with an embedding vector, and for each token, we utilize a bidirectional LSTM (Bi-LSTM) to encode the labels of its previous tokens for modeling internal context information and label dependence. Afterward, we propose a Bi-Attention module that merges the token representation from a pre-trained model and the label features from the Bi-LSTM as the label-aware information, which is concatenated to the token representation to facilitate cross-domain NER. In doing so, label information contained in the embedding vectors can be effectively transferred to the target domain, and Bi-LSTM can further model the label relationship among different domains by pre-train and then fine-tune setting. Experimental results on several datasets confirm the effectiveness of our model, where our model achieves significant improvements over the state of the arts.

2021

pdf bib
Word Graph Guided Summarization for Radiology Findings
Jinpeng Hu | Jianling Li | Zhihong Chen | Yaling Shen | Yan Song | Xiang Wan | Tsung-Hui Chang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021