Papers by Junfan Chen
Prototype-Guided Pseudo Labeling for Semi-Supervised Text Classification (2023.acl-long)
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| Challenge: | Existing semi-supervised text classification methods suffer from categorical boundary issues . existing methods suffer by ambiguous categoric boundaries, making it difficult to generate reliable pseudo-labels for each category. |
| Approach: | They propose a semi-supervised framework that assigns pseudo-labels to unlabeled data . they exploit categorical prototypes to assimilate instance representations within the same category . |
| Outcome: | Empirical studies show that the proposed framework is effective . it uses prototypical cluster separation and prototypical-center data selection . |
E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models (2022.coling-1)
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Ling Ge, ChunMing Hu, Guanghui Ma, Junshuang Wu, Junfan Chen, JiHong Liu, Hong Zhang, Wenyi Qin, Richong Zhang
| Challenge: | Empirical studies show that our approach outperforms the SOTA methods in improving the interpretability of text classification models. |
| Approach: | They propose an enhanced variational word masks approach that exploits the Variational Information Bottleneck to obtain task-specific words. |
| Outcome: | Empirical results show that the proposed method outperforms the SOTA methods in improving the interpretability of the model. |
Neural Dialogue State Tracking with Temporally Expressive Networks (2020.findings-emnlp)
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| Challenge: | Existing models ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. |
| Approach: | They propose to combine temporal feature dependencies in spoken dialogues by using recurrent networks and probabilistic graphical models. |
| Outcome: | The proposed model improves turn-level-state prediction and state aggregation on standard datasets. |
Improving Data Annotation for Low-Resource Relation Extraction with Logical Rule-Augmented Collaborative Language Models (2025.naacl-long)
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| Challenge: | Low-resource relation extraction aims to identify semantic relationships using scarce labeled data. |
| Approach: | They propose a framework that iteratively integrates high-confidence predictions of rule-enhanced relation extractors with varying scales to obtain reliable pseudo annotations from massive unlabeled samples without human supervision. |
| Outcome: | The proposed framework achieves state-of-the-art on benchmark datasets in few-shot scenarios. |
Text Style Transferring via Adversarial Masking and Styled Filling (2022.emnlp-main)
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| Challenge: | Existing models for text style transfer suffer from two challenges: the word masking procedure may mistakenly remove unexpected words and the selected words in the word filling procedure lack diversity and semantic consistency. |
| Approach: | They propose a style transfer model with adversarial masking and styled filling techniques to solve these challenges. |
| Outcome: | The proposed model performs well on two benchmark text style transfer data sets. |
A Hierarchical N-Gram Framework for Zero-Shot Link Prediction (2022.findings-emnlp)
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| Challenge: | Existing approaches to zero-shot link prediction use textual features of relations as auxiliary information to improve the encoded representation. |
| Approach: | They propose a Hierarchical N-gram framework for Zero-Shot Link Prediction that leverages character n-gram information for ZSLP. |
| Outcome: | The proposed method achieves state-of-the-art on two standard ZSLP datasets. |
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (D19-1)
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| Challenge: | Existing methods for relation extraction assume that text is noisy, but its corresponding labels are clean. |
| Approach: | They propose a framework that combines neural network and probabilistic modelling to denoise noisy relation labels. |
| Outcome: | The proposed framework improves the current art in uncovering the ground-truth relation labels. |
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition (2022.acl-long)
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| Challenge: | Existing models for named entity recognition only consider the potential transferability between two identical tasks across both domains. |
| Approach: | They propose to use a similarity metric model to improve cross-lingual named entity recognition task on target domain. |
| Outcome: | Empirical studies on 7 different languages confirm the effectiveness of the proposed model. |
Tucker Decomposition with Frequency Attention for Temporal Knowledge Graph Completion (2023.findings-acl)
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| Challenge: | Existing models for temporal knowledge graph completion only consider the combination of one relation with one timestamp, ignoring the global nature of the embedding. |
| Approach: | They propose a temporal knowledge Graph Completion model that captures global temporal dependencies between one relation and the entire timestamp. |
| Outcome: | The proposed model outperforms the state-of-the-art models on three standard TKGC datasets on several metrics. |
Adversarial Metric Learning for Fine-Grained Emotion Classification (2026.acl-long)
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| Challenge: | Recent advances in fine-grained emotion classification relied on contrastive learning with hard-pair mining. |
| Approach: | They propose an adversarial metric learning framework that replaces fixed similarity metrics with a learnable metric family and trains representations to remain discriminative under worst-case similarity distortions. |
| Outcome: | The proposed framework trains a pairwise discriminator to maximally confuse two hard pair types while training the encoder to remain discriminative under worst-case similarity distortions. |
Hypernym Discovery via a Recurrent Mapping Model (2021.findings-acl)
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| Challenge: | Empirical studies on SemEval-2018 Task 9 confirm the effectiveness of the presented model. |
| Approach: | They propose a parallel style model that maps query words to their hypernyms . they use a lexical-semantic relation to name a specific instance or subtype hyponym . |
| Outcome: | Empirical results on SemEval-2018 Task 9 confirm the effectiveness of the proposed model. |
Open-Set Semi-Supervised Text Classification via Adversarial Disagreement Maximization (2024.acl-long)
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| Challenge: | Open-Set Semi-Supervised Text Classification (OSTC) aims to train a classification model on a limited set of labeled texts along with plenty of unlabeled examples. |
| Approach: | They propose to train a classification model on a limited set of labeled texts alongside plenty of unlabeled examples that include both in-distribution and out-of-difference examples. |
| Outcome: | The proposed model improves on outlier detection and abnormal example detection and calibration. |
Progressively Modality Freezing for Multi-Modal Entity Alignment (2024.acl-long)
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| Challenge: | Multi-Modal Entity Alignment aims to discover identical entities across heterogeneous knowledge graphs. |
| Approach: | They propose a strategy of progressive modality freezing that focuses on alignment-relevant features and enhances multi-modal feature fusion. |
| Outcome: | The proposed approach demonstrates state-of-the-art performance and the rationale for freezing modalities. |
Calibrating Pseudo-Labeling with Class Distribution for Semi-supervised Text Classification (2025.emnlp-main)
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| Challenge: | Existing studies develop effective pseudo-labeling methods, but they struggle with unlabeled data that have imbalanced classes mismatched with the labeled data. |
| Approach: | They propose to use pseudo-labeling to train text classification models with few labeled data and massive unlabeled data. |
| Outcome: | Empirical results show that the proposed model outperforms state-of-the-art methods on 3 common benchmarks. |
Parameter-free Automatically Prompting: A Latent Pseudo Label Mapping Model for Prompt-based Learning (2022.findings-emnlp)
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| Challenge: | Existing manual label mapping methods that require extra parameters and human knowledge are limited in data. |
| Approach: | They propose a Latent Pseudo Label Mapping method that optimizes the label mapping without human knowledge and extra parameters. |
| Outcome: | The proposed method outperforms the standard SOTA method in few-shot learning tasks and significantly outperformed the standard ALM method which requires extra task-specific prior knowledge. |
Parallel Interactive Networks for Multi-Domain Dialogue State Generation (2020.emnlp-main)
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| Challenge: | Existing models do not account for the dependencies between system and user utterances in the same turn and across different turns. |
| Approach: | They propose to integrate an interactive encoder to jointly model in-turn dependencies and cross-turn dependents. |
| Outcome: | The proposed model is superior to existing models and can be used to selectively copy words from historical system utterances or historical user utterrances. |