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Found 17 papers, 5 papers with code
Towards Enhancing Faithfulness for Neural Machine Translation
Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences.
The Rise and Potential of Large Language Model Based Agents: A Survey
Many efforts have been made to develop intelligent agents, but they mainly focus on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks.
Secrets of RLHF in Large Language Models Part I: PPO
Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model.
Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning
A contributing factor to this problem is that NMT models trained with the one-to-one paradigm struggle to handle the source diversity phenomenon, where inputs with the same meaning can be expressed differently.
Learning Decoupled Retrieval Representation for Nearest Neighbour Neural Machine Translation
Generally, kNN-MT borrows the off-the-shelf context representation in the translation task, e. g., the output of the last decoder layer, as the query vector of the retrieval task.
Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation
Although data augmentation is widely used to enrich the training data, conventional methods with discrete manipulations fail to generate diverse and faithful training samples.
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation
As a sequence-to-sequence generation task, neural machine translation (NMT) naturally contains intrinsic uncertainty, where a single sentence in one language has multiple valid counterparts in the other.
On Learning Universal Representations Across Languages
Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks.
Contrastive Learning
Cross-Lingual Natural Language Inference
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Multiscale Collaborative Deep Models for Neural Machine Translation
Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train.
AR: Auto-Repair the Synthetic Data for Neural Machine Translation
Compared with only using limited authentic parallel data as training corpus, many studies have proved that incorporating synthetic parallel data, which generated by back translation (BT) or forward translation (FT, or selftraining), into the NMT training process can significantly improve translation quality.
GRET: Global Representation Enhanced Transformer
The encoder maps the words in the input sentence into a sequence of hidden states, which are then fed into the decoder to generate the output sentence.
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation
The standard paradigm of exploiting them includes two steps: first, pre-training a model, e. g. BERT, with a large scale unlabeled monolingual data.
Improving Neural Machine Translation with Pre-trained Representation
Then, we design a framework for integrating both source and target sentence-level representations into NMT model to improve the translation quality.
Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering
However, one critical problem is that current approaches only get high accuracy for questions whose relations have been seen in the training data.
Correct-and-Memorize: Learning to Translate from Interactive Revisions
Experiments in both ideal and real interactive translation settings demonstrate that our proposed \method enhances machine translation results significantly while requires fewer revision instructions from human compared to previous methods.
Learning to Discriminate Noises for Incorporating External Information in Neural Machine Translation
Previous studies show that incorporating external information could improve the translation quality of Neural Machine Translation (NMT) systems.
Neural Machine Translation with Word Predictions
In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information about the sentence. These vectors are generated by parameters which are updated by back-propagation of translation errors through time.
