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Found 8 papers, 1 papers with code
TANDO: A Corpus for Document-level Machine Translation
Document-level Neural Machine Translation aims to increase the quality of neural translation models by taking into account contextual information.
Online Learning over Time in Adaptive Neural Machine Translation
Adaptive Machine Translation purports to dynamically include user feedback to improve translation quality.
Does Context Help Mitigate Gender Bias in Neural Machine Translation?
Neural Machine Translation models tend to perpetuate gender bias present in their training data distribution.
Promoting Target Data in Context-aware Neural Machine Translation
Standard context-aware neural machine translation (NMT) typically relies on parallel document-level data, exploiting both source and target contexts.
Split and Rephrase with Large Language Models
Our results provide a fine-grained analysis of the potential and limitations of large language models for SPRP, with significant improvements achievable using relatively small amounts of training data and model parameters overall, and remaining limitations for all models on the task.
Handle with Care: A Case Study in Comparable Corpora Exploitation for Neural Machine Translation
We present the results of a case study in the exploitation of comparable corpora for Neural Machine Translation.
To Case or not to case: Evaluating Casing Methods for Neural Machine Translation
We present a comparative evaluation of casing methods for Neural Machine Translation, to help establish an optimal pre- and post-processing methodology.
