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- Modeling Coverage for Neural Machine Translation786
This paper proposes coverage-based NMT, which maintains a coverage vector to keep track of the attention history and improves both translation quality and alignment quality over standard attention- based NMT.
- Tree-to-string alignment template for statistical machine translation394
A novel translation model based on tree-to-string alignment template (TAT) which describes the alignment between a source parse tree and a target string that significantly outperforms Pharaoh, a state-of-the-art decoder for phrase-based models.
- Improving the Transformer Translation Model with Document-Level Context274
This work extends the Transformer model with a new context encoder to represent document-level context, which is then incorporated into the original encoder and decoder, and introduces a two-step training method to take full advantage of abundant sentence-level parallel corpora and limited document- level parallel Corpora.
- Neural Machine Translation with Reconstruction212
Experiments show that the proposed framework significantly improves the adequacy of NMT output and achieves superior translation result over state-of-the-art NMT and statistical MT systems.
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- Learning to Remember Translation History with a Continuous Cache189
This work proposes to augment NMT models with a very light-weight cache-like memory network, which stores recent hidden representations as translation history and the probability distribution over generated words is updated online depending on the translation history retrieved from the memory.
- Visualizing and Understanding Neural Machine Translation186
This work proposes to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoder-decoder framework and shows that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors.
- Towards Robust Neural Machine Translation169
Experimental results on Chinese-English, English-German and English-French translation tasks show that the proposed approaches can not only achieve significant improvements over strong NMT systems but also improve the robustness of NMT models.
- Learning Structured Text Representations156
A model that can encode a document while automatically inducing rich structural dependencies is proposed that embeds a differentiable non-projective parsing algorithm into a neural model and uses attention mechanisms to incorporate the structural biases.
- Neural machine translation: A review of methods, resources, and tools152
A broad review of the methods for NMT is provided and focus on methods relating to architectures, decoding, and data augmentation, with a discussion of possible future research directions.
- Context Gates for Neural Machine Translation152
Context gates are proposed which dynamically control the ratios at which source and target contexts contribute to the generation of target words and can enhance both the adequacy and fluency of NMT with more careful control of the information flow from contexts.
- Skywork: A More Open Bilingual Foundation Model135
This bilingual foundation model is the most extensively trained and openly published LLMs of comparable size to date, and introduces a two-stage training methodology using a segmented corpus, targeting general purpose training and then domain-specific enhancement training, respectively.
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- THUMT: An Open-Source Toolkit for Neural Machine Translation95
Experiments on English-German and Chinese-English datasets show that THUMT can obtain results that are comparable to state-of-the-art NMT systems.
- On the Inference Calibration of Neural Machine Translation94
This work provides in-depth analyses of the correlation between calibration and translation performance as well as linguistic properties of miscalibration and reports a number of interesting findings that might help humans better analyze, understand and improve NMT models.
- Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach87
This work proposes a contrastive learning approach to reducing word omission errors in NMT that achieves better translation performance than three baseline methods and designs different types of negative examples depending on the number of omitted words, word frequency, and part of speech.
- Improving Back-Translation with Uncertainty-based Confidence Estimation86
This work proposes to quantify the confidence of NMT model predictions based on model uncertainty, and shows that uncertainty-based confidence estimation significantly improves the performance of back-translation.
- Analysis of the Paragraph Vector Model for Information Retrieval84
The unregulated training process of PV is vulnerable to short document over-fitting that produces length bias in the final retrieval model, and the corpus-based negative sampling of PV leads to a weighting scheme for words that overly suppresses the importance of frequent words.
- 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.
- Automated Essay Scoring with Discourse-Aware Neural Models79
Experiments on three essay scoring tasks show benefits from all three strategies in different combinations, with simpler architectures being more effective when less training data is available.
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- Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization71
This work proposes to use posterior regularization to provide a general framework for integrating prior knowledge into neural machine translation, and represents prior knowledge sources as features in a log-linear model, which guides the learning processing of the neural translation model.
- Recursive Autoencoders for ITG-Based Translation68
This work proposes to use recursive autoencoders to make full use of the entire merging blocks alternatively to enable predicting orders to exploit syntactic and semantic information from a neural language modeling’s perspective.
- Accurate Word Alignment Induction from Neural Machine Translation66
It is shown that attention weights do capture accurate word alignment, which could only be revealed if the correct decoding step and layer to induce word alignment is chosen, and two simple but effective interpretation methods for word alignment induction are presented.
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 2026-10-16. 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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