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Junfan Chen

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9 papers

1 author row

AAAI Conference 2026 Conference Paper

Rethinking Label Consistency of In-Context Learning: An Implicit Transductive Label Propagation Perspective

Haoyang Chen
Richong Zhang
Junfan Chen

Large language models (LLMs) perform in-context learning (ICL) with minimal supervised examples, which benefits various natural language processing (NLP) tasks. One of the critical research focus is the selection of prompt demonstrations. Current approaches typically employ retrieval models to select the top-K most semantically similar examples as demonstrations. However, we argue that existing methods are limited since the label consistency is not guaranteed during demonstration selection. Our cognition derives from the Bayesian view of ICL and our rethinking of ICL from the transductive label propagation perspective. We treat ICL as a transductive learning method and incorporate latent concepts from Bayesian view and deduce that similar demonstrations guide the concepts of query, with consistent labels serving as estimates. Based on this understanding, we establish a label propagation framework to link label consistency with propagation error bounds. To model label consistency, we propose a data synthesis method, leveraging both semantic and label information, and use TopK sampling with Synthetic Data (TopK-SD) to acquire demonstrations with consistent labels. TopK-SD outperforms original TopK sampling on multiple benchmarks. Our work provides a new perspective for understanding the working mechanisms within ICL.

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AAAI Conference 2025 Conference Paper

Implicit Word Reordering with Knowledge Distillation for Cross-Lingual Dependency Parsing

Zhuoran Li
Chunming Hu
Junfan Chen
Zhijun Chen
Richong Zhang

Word order difference between source and target languages is a major obstacle to cross-lingual transfer, especially in the dependency parsing task. Current works are mostly based on order-agnostic models or word reordering to mitigate this problem. However, such methods either do not leverage grammatical information naturally contained in word order or are computationally expensive as the permutation space grows exponentially with the sentence length. Moreover, the reordered source sentence with an unnatural word order may be a form of noising that harms the model learning. To this end, we propose an Implicit Word Reordering framework with Knowledge Distillation (IWR-KD). This framework is inspired by that deep networks are good at learning feature linearization corresponding to meaningful data transformation, e.g. word reordering. To realize this idea, we introduce a knowledge distillation framework composed of a word-reordering teacher model and a dependency parsing student model. We verify our proposed method on Universal Dependency Treebanks across 31 different languages and show it outperforms a series of competitors, together with experimental analysis to illustrate how our method works towards training a robust parser.

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AAAI Conference 2025 Conference Paper

Momentum Pseudo-Labeling for Weakly Supervised Phrase Grounding

Dongdong Kuang
Richong Zhang
Zhijie Nie
Junfan Chen
Jaein Kim

Weakly supervised phrase grounding tasks aim to learn alignments between phrases and regions with coarse image-caption match information. One branch of previous methods established pseudo-label relationships between phrases and regions based on the Expectation-Maximization (EM) algorithm combined with contrastive learning. However, adopting a simplified batch-level local update (partial) of pseudo-labels in E-step is sub-optimal, while extending it to global update requires inefficiently numerous computations. In addition, their failure to consider potential false negative examples in contrastive loss negatively impacts the effectiveness of M-step optimization. To address these issues, we propose a Momentum Pseudo Labeling (MPL) method, which efficiently uses a momentum model to synchronize global pseudo-label updates on the fly with model parameter updating. Additionally, we explore potential relationships between phrases and regions from non-matching image-caption pairs and convert these false negative examples to positive ones in contrastive learning. Our approach achieved SOTA performance on 3 commonly used grounding datasets for weakly supervised phrase grounding tasks.

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AAAI Conference 2025 Conference Paper

Semi-Supervised Multimodal Classification Through Learning from Modal and Strategic Complementarities

Junchi Chen
Richong Zhang
Junfan Chen

Supervised multimodal classification has been proven to outperform unimodal classification in the image-text domain. However, this task is highly dependent on abundant labeled data. To perform multimodal classification in data-insufficient scenarios, in this study, we explore semi-supervised multimodal classification (SSMC) that only requires a small amount of labeled data and plenty of unlabeled data. Specifically, we first design baseline SSMC models by combining known semi supervised pseudo-labeling methods with the two most commonly used modal fusion strategies, i.e. feature-level fusion and label-level aggregation. Based on our investigation and empirical study of the baselines, we discover two complementarities that may benefit SSMC if properly exploited: the predictions from different modalities (modal complementarity) and modal fusion strategies for pseudo-labeling (strategic complementarity). Therefore, we propose a Modal and Strategic Complementarity (MSC) framework for SSMC. Concretely, to exploit modal complementarity, we propose to learn reliability weights for the predictions from different modalities and refine the fusion scores. To learn from strategic complementarity, we introduce a dual KL divergence loss to guide the balance of quantity and quality of pseudo-labeled data selection. Extensive empirical studies demonstrate the effectiveness of the proposed framework.

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IJCAI Conference 2024 Conference Paper

Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching

Zhuoran Li
Chunming Hu
Junfan Chen
Zhijun Chen
Xiaohui Guo
Richong Zhang

Code-switching is a data augmentation scheme mixing words from multiple languages into source lingual text. It has achieved considerable generalization performance of cross-lingual transfer tasks by aligning cross-lingual contextual word representations. However, uncontrolled and over-replaced code-switching would augment dirty samples to model training. In other words, the excessive code-switching text samples will negatively hurt the models' cross-lingual transferability. To this end, we propose a Progressive Code-Switching (PCS) method to gradually generate moderately difficult code-switching examples for the model to discriminate from easy to hard. The idea is to incorporate progressively the preceding learned multilingual knowledge using easier code-switching data to guide model optimization on succeeding harder code-switching data. Specifically, we first design a difficulty measurer to measure the impact of replacing each word in a sentence based on the word relevance score. Then a code-switcher generates the code-switching data of increasing difficulty via a controllable temperature variable. In addition, a training scheduler decides when to sample harder code-switching data for model training. Experiments show our model achieves state-of-the-art results on three different zero-shot cross-lingual transfer tasks across ten languages.

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AAAI Conference 2023 Conference Paper

Adversarial Word Dilution as Text Data Augmentation in Low-Resource Regime

Junfan Chen
Richong Zhang
Zheyan Luo
Chunming Hu
Yongyi Mao

Data augmentation is widely used in text classification, especially in the low-resource regime where a few examples for each class are available during training. Despite the success, generating data augmentations as hard positive examples that may increase their effectiveness is under-explored. This paper proposes an Adversarial Word Dilution (AWD) method that can generate hard positive examples as text data augmentations to train the low-resource text classification model efficiently. Our idea of augmenting the text data is to dilute the embedding of strong positive words by weighted mixing with unknown-word embedding, making the augmented inputs hard to be recognized as positive by the classification model. We adversarially learn the dilution weights through a constrained min-max optimization process with the guidance of the labels. Empirical studies on three benchmark datasets show that AWD can generate more effective data augmentations and outperform the state-of-the-art text data augmentation methods. The additional analysis demonstrates that the data augmentations generated by AWD are interpretable and can flexibly extend to new examples without further training.

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AAAI Conference 2023 Conference Paper

Multi-Mask Label Mapping for Prompt-Based Learning

Jirui Qi
Richong Zhang
Jaein Kim
Junfan Chen
Wenyi Qin
Yongyi Mao

Prompt-based Learning has shown significant success in few-shot classification. The mainstream approach is to concatenate a template for the input text to transform the classification task into a cloze-type task where label mapping plays an important role in finding the ground-truth labels. While current label mapping methods only use the contexts in one single input, it could be crucial if wrong information is contained in the text. Specifically, it is proved in recent work that even the large language models like BERT/RoBERTa make classification decisions heavily dependent on a specific keyword regardless of the task or the context. Such a word is referred to as a lexical cue and if a misleading lexical cue is included in the instance it will lead the model to make a wrong prediction. We propose a multi-mask prompt-based approach with Multi-Mask Label Mapping (MMLM) to reduce the impact of misleading lexical cues by allowing the model to exploit multiple lexical cues. To satisfy the conditions of few-shot learning, an instance augmentation approach for the cloze-type model is proposed and the misleading cues are gradually excluded through training. We demonstrate the effectiveness of MMLM by both theoretical analysis and empirical studies, and show that MMLM outperforms other existing label mapping approaches.

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AAAI Conference 2022 Conference Paper

ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification

Junfan Chen
Richong Zhang
Yongyi Mao
Jie Xu

Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. Despite their success, existing works building their meta-learner based on Prototypical Networks are unsatisfactory in learning discriminative text representations between similar classes, which may lead to contradictions during label prediction. In addition, the tasklevel and instance-level overfitting problems in few-shot text classification caused by a few training examples are not sufficiently tackled. In this work, we propose a contrastive learning framework named ContrastNet to tackle both discriminative representation and overfitting problems in few-shot text classification. ContrastNet learns to pull closer text representations belonging to the same class and push away text representations belonging to different classes, while simultaneously introducing unsupervised contrastive regularization at both task-level and instance-level to prevent overfitting. Experiments on 8 few-shot text classification datasets show that ContrastNet outperforms the current state-of-the-art models.

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AAAI Conference 2021 Conference Paper

On Scalar Embedding of Relative Positions in Attention Models

Junshuang Wu
Richong Zhang
Yongyi Mao
Junfan Chen

Attention with positional encoding has been demonstrated as a powerful component in modern neural network models, such as transformers. However, why positional encoding works well in attention models remains largely unanswered. In this paper, we study the scalar relative positional encoding (SRPE) proposed in the T5 transformer. Such an encoding method has two features. First, it uses a scalar to embed relative positions. Second, the relative positions are bucketized using a fixed heuristic algorithm, and positions in the same bucket share the same embedding. In this work, we show that SRPE in attention has an elegant probabilistic interpretation. More specifically, the positional encoding serves to produce a prior distribution for the attended positions. The resulting attentive distribution can be viewed as a posterior distribution of the attended position given the observed input sequence. Furthermore, we propose a new SRPE (AT5) that adopts a learnable bucketization protocol and automatically adapts to the dependency range specific to the learning task. Empirical studies show that the AT5 achieves superior performance than the T5’s SRPE.

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