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- Enhong ChenSuggested from co-authorship
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Works88 from public data
- 521
- Document-Level Machine Translation with Large Language Models219
An in-depth evaluation of LLMs' ability on discourse modeling using document-level machine translation (MT) as a testbed finds that LLMs have demonstrated superior performance and show potential to become a new paradigm for document- level translation.
- Regularizing Neural Machine Translation by Target-Bidirectional Agreement121
A novel model regularization method is proposed for NMT training, which aims to improve the agreement between translations generated by left-to-right (L2R) and right-to-left (R2L) NMT decoders, and significantly outperforms state-of-the-art baselines on Chinese-English and English-German translation tasks.
- Joint Training for Neural Machine Translation Models with Monolingual Data119
Experimental results on Chinese-English and English-German translation tasks show that the proposed approach can simultaneously improve translation quality of source-to-target and target- to-source models, significantly outperforming strong baseline systems which are enhanced with monolingual data for model training including back-translation.
- Adaptive Nearest Neighbor Machine Translation107
The proposed Adaptive kNN-MT to dynamically determine the number of k for each target token is proposed by introducing a light-weight Meta-k Network, which can be efficiently trained with only a few training samples.
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- Fairness-guided Few-shot Prompting for Large Language Models87
This paper introduces a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes and proposes a novel search strategy based on the greedy search to identify the near-optimal prompt for improving the performance of in-context learning.
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- Cross-Lingual Pre-Training Based Transfer for Zero-Shot Neural Machine Translation68
This work proposes an effective transfer learning approach based on cross-lingual pre-training to enable a smooth transition for zero-shot translation and significantly outperforms strong pivot-based baseline and various multilingual NMT approaches.
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- Unsupervised Neural Machine Translation with SMT as Posterior Regularization61
The phrase based Statistic Machine Translation models, which are robust to noisy data, are introduced, as posterior regularizations to guide the training of unsupervised NMT models in the iterative back-translation process, and can compensate for the deficiency of fluency inherent in SMT.
- Learning to Collaborate for Question Answering and Asking51
A training algorithm is presented that generalizes both Generative Adversarial Network (GAN) and Generative Domain-Adaptive Nets (GDAN) under the question answering scenario and finds that the performance of a QG model could be easily improved by a QA model via policy gradient.
- Interactive genetic color matching design of cultural and creative products considering color image and visual aesthetics44
An interactive genetic algorithm is developed to promote and optimize the color scheme of cultural and creative products, and a fitness function based on subjective image evaluation and objective visual aesthetics is constructed.
- Task-Oriented Dialogue System as Natural Language Generation43
A novel GPT-Adapter-CopyNet network is designed, which incorporates the lightweight adapter and CopyNet modules into G PT-2 to achieve better performance on transfer learning and dialogue entity generation.
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- Budgeted Policy Learning for Task-Oriented Dialogue Systems38
This paper presents a new approach that extends Deep Dyna-Q by incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed, small amount of user interactions (budget) for learning task-oriented dialogue agents.
- Bidirectional Generative Adversarial Networks for Neural Machine Translation34
A novel Bidirectional Generative Adversarial Network for Neural Machine Translation (BGAN-NMT) is proposed, which aims to introduce a generator model to act as the discriminator, whereby the discriminators naturally considers the entire translation space so that the inadequate training problem can be alleviated.
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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 2026-10-13. 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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