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- Min ZhangSuggested from co-authorship
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Works230 from public data
- Variational Neural Machine Translation211
This paper builds a neural posterior approximator conditioned on both the source and the target sides, and equip it with a reparameterization technique to estimate the variational lower bound, and shows that the proposed variational neural machine translation achieves significant improvements over the vanilla neural machinetranslation baselines.
- A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation177
A novel graph-based multi-modal fusion encoder for NMT that captures various semantic relationships between multi- modal semantic units (words and visual objects) and provides an attention-based context vector for the decoder.
- Cascade Grouped Attention Network for Referring Expression Segmentation160
A Cascade Grouped Attention Network with two innovative designs: Cascade Grouping Attention (CGA) and Instance-level Attention (ILA) loss that can achieve the high efficiency of one-stage RES while possessing a strong reasoning ability comparable to the two-stage methods.
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- Accelerating Neural Transformer via an Average Attention Network129
The proposed average attention network is applied on the decoder part of the neural Transformer to replace the original target-side self-attention model and enables the neuralTransformer to decode sentences over four times faster than its original version with almost no loss in training time and translation performance.
- Asynchronous Bidirectional Decoding for Neural Machine Translation125
This paper equips the conventional attentional encoder-decoder NMT framework with a backward decoder, in order to explore bidirectional decoding for NMT, and achieves substantial improvements over the conventional NMT by 3.14 and 1.38 BLEU points, respectively.
- Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition112
A Shallow Convolutional Neural Network (SCNN) is proposed, which contains only one hidden layer but is effective in relation recognition, which alleviates the overfitting problem, while the convolution and nonlinear operations help preserve the recognition and generalization ability of the model.
- Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis92
A progressive self-supervised attention learning approach for neural ASC models, which automatically mines useful attention supervision information from a training corpus to refine attention mechanisms, and augment the conventional training objective with a regularization term.
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- A Real-Time Global Inference Network for One-Stage Referring Expression Comprehension86
The proposed RealGIN outperforms most existing methods and achieves very competitive performances against the most advanced one, i.e., MAttNet, under the same hardware, and can boost the processing speed by 10–20 times over the existing methods.
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- Variational Recurrent Neural Machine Translation84
A novel variational recurrent neural machine translation model that introduces a series of latent random variables to model the translation procedure of a sentence in a generative way, instead of a single latent variable.
- Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination81
This paper jointly model NMT with monolingual attention-based domain classification tasks and improves NMT as follows, distinguishing and exploiting word-level domain contexts for multi-domain NMT.
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- Exploiting Temporal Relationships in Video Moment Localization with Natural Language78
A novel Temporal Compositional Modular Network (TCMN) where a tree attention network first automatically decomposes a sentence into three descriptions with respect to the main event, context event and temporal signal is proposed.
- Graph-based Neural Sentence Ordering71
A novel and flexible graph-based neural sentence ordering model, which adopts graph recurrent network \cite{Zhang:acl18} to accurately learn semantic representations of the sentences, which outperforms the existing state-of-the-art systems on several benchmark datasets.
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- Enhanced aspect-based sentiment analysis models with progressive self-supervised attention learning66
This work proposes a progressive self-supervised attention learning approach for attentional ABSA models that enhances the performance of all three models and integrates the proposed approach into three state-of-the-art neuralABSA models.
- Structural Information Preserving for Graph-to-Text Generation59
This work introduces two types of autoencoding losses, each individually focusing on different aspects (a.k.a. views) of input graphs, that can guide the model for preserving input information via multi-task training.
- When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation57
This work proposes GraphRAG-Bench, a comprehensive benchmark designed to evaluate GraphRAG models on both hierarchical knowledge retrieval and deep contextual reasoning, and systematically investigates the conditions when GraphRAG surpasses traditional RAG.
- A Robust Adversarial Training Approach to Machine Reading Comprehension55
This paper proposes a novel robust adversarial training approach to improve the robustness of MRC models in a more generic way and boosts the overall performance on adversarial datasets and outperforms the state-of-the-art methods.
- Translation Model Adaptation for Statistical Machine Translation with Monolingual Topic Information53
This paper proposes a novel approach for translation model adaptation by utilizing in-domain monolingual topic information instead of the in- domain bilingual corpora, which incorporates the topic information into translation probability estimation.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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