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Found 21 papers, 11 papers with code
The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020
We present Sockeye 2, a modernized and streamlined version of the Sockeye neural machine translation (NMT) toolkit.
CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality
However, in many cases, multiple different translations are valid and the appropriate translation may depend on the intended target audience, characteristics of the speaker, or even the relationship between speakers.
Sockeye 3: Fast Neural Machine Translation with PyTorch
When running comparable models, Sockeye 3 is up to 126% faster than other PyTorch implementations on GPUs and up to 292% faster on CPUs.
End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation
Conventional speech-to-text translation (ST) systems are trained on single-speaker utterances, and they may not generalize to real-life scenarios where the audio contains conversations by multiple speakers.
Multi-Task Neural Models for Translating Between Styles Within and Across Languages
Generating natural language requires conveying content in an appropriate style.
Controlling Neural Machine Translation Formality with Synthetic Supervision
This work aims to produce translations that convey source language content at a formality level that is appropriate for a particular audience.
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation
As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased.
Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation
Despite some empirical success at correcting exposure bias in machine translation, scheduled sampling algorithms suffer from a major drawback: they incorrectly assume that words in the reference translations and in sampled sequences are aligned at each time step.
Identifying Semantic Divergences in Parallel Text without Annotations
Recognizing that even correct translations are not always semantically equivalent, we automatically detect meaning divergences in parallel sentence pairs with a deep neural model of bilingual semantic similarity which can be trained for any parallel corpus without any manual annotation.
Faithful Target Attribute Prediction in Neural Machine Translation
The training data used in NMT is rarely controlled with respect to specific attributes, such as word casing or gender, which can cause errors in translations.
Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation
We aim to better exploit the limited amounts of parallel text available in low-resource settings by introducing a differentiable reconstruction loss for neural machine translation (NMT).
Bi-Directional Neural Machine Translation with Synthetic Parallel Data
Despite impressive progress in high-resource settings, Neural Machine Translation (NMT) still struggles in low-resource and out-of-domain scenarios, often failing to match the quality of phrase-based translation.
A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output
Stylistic variations of language, such as formality, carry speakers{'} intention beyond literal meaning and should be conveyed adequately in translation.
Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation
Parallel corpora are often not as parallel as one might assume: non-literal translations and noisy translations abound, even in curated corpora routinely used for training and evaluation.
Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases
Detecting and analyzing stylistic variation in language is relevant to diverse Natural Language Processing applications.
Evaluating Robustness to Input Perturbations for Neural Machine Translation
Neural Machine Translation (NMT) models are sensitive to small perturbations in the input.
Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation
While Iterative Back-Translation and Dual Learning effectively incorporate monolingual training data in neural machine translation, they use different objectives and heuristic gradient approximation strategies, and have not been extensively compared.
The UMD Machine Translation Systems at IWSLT 2016: English-to-French Translation of Speech Transcripts
We describe the University of Maryland machine translation system submitted to the IWSLT 2016 Microsoft Speech Language Translation (MSLT) English-French task.
Findings of the IWSLT 2022 Evaluation Campaign
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.
Pseudo-Label Training and Model Inertia in Neural Machine Translation
While the effect of PLT on quality is well-documented, we highlight a lesser-known effect: PLT can enhance a model's stability to model updates and input perturbations, a set of properties we call model inertia.
RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation
Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of translation outputs.
