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Found 7 papers, 0 papers with code
Document-Level Language Models for Machine Translation
Finally, we explore language model fusion in the light of recent advancements in large language models.
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer
Pivot-based neural machine translation (NMT) is commonly used in low-resource setups, especially for translation between non-English language pairs.
Integrated Training for Sequence-to-Sequence Models Using Non-Autoregressive Transformer
Complex natural language applications such as speech translation or pivot translation traditionally rely on cascaded models.
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages
We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i. e., source-pivot and pivot-target, leading to a significant improvement in source-target translation.
Word-based Domain Adaptation for Neural Machine Translation
Pre-training a model with word weights improves fine-tuning up to 1. 24% BLEU absolute and 1. 64% TER, respectively.
Learning from Chunk-based Feedback in Neural Machine Translation
We empirically investigate learning from partial feedback in neural machine translation (NMT), when partial feedback is collected by asking users to highlight a correct chunk of a translation.
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search
In this paper, we introduce a hybrid search for attention-based neural machine translation (NMT).
