Machine Translation
2152 papers with code • 80 benchmarks • 77 datasets
Machine translation is the task of translating a sentence in a source language to a different target language.
Approaches for machine translation can range from rule-based to statistical to neural-based. More recently, encoder-decoder attention-based architectures like BERT have attained major improvements in machine translation.
One of the most popular datasets used to benchmark machine translation systems is the WMT family of datasets. Some of the most commonly used evaluation metrics for machine translation systems include BLEU, METEOR, NIST, and others.
( Image credit: Google seq2seq )
Benchmarks
These leaderboards are used to track progress in Machine Translation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Machine Translation models and implementations
WMT 2014
OpenSubtitles
WMT 2016
MLQA
Europarl
ASPEC
MTNT
Subtasks
Latest papers with no code
Introducing L2M3, A Multilingual Medical Large Language Model to Advance Health Equity in Low-Resource Regions
Addressing the imminent shortfall of 10 million health workers by 2030, predominantly in Low- and Middle-Income Countries (LMICs), this paper introduces an innovative approach that harnesses the power of Large Language Models (LLMs) integrated with machine translation models.
Charles Translator: A Machine Translation System between Ukrainian and Czech
We present Charles Translator, a machine translation system between Ukrainian and Czech, developed as part of a society-wide effort to mitigate the impact of the Russian-Ukrainian war on individuals and society.
An inclusive review on deep learning techniques and their scope in handwriting recognition
This paper presents a survey on the existing studies of deep learning in handwriting recognition field.
Interplay of Machine Translation, Diacritics, and Diacritization
We examine these two questions in both high-resource (HR) and low-resource (LR) settings across 55 different languages (36 African languages and 19 European languages).
Exploring the Necessity of Visual Modality in Multimodal Machine Translation using Authentic Datasets
Recent research in the field of multimodal machine translation (MMT) has indicated that the visual modality is either dispensable or offers only marginal advantages.
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods
In various real-world applications such as machine translation, sentiment analysis, and question answering, a pivotal role is played by NLP models, facilitating efficient communication and decision-making processes in domains ranging from healthcare to finance.
Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation
Parameter-efficient fine-tuning (PEFT) methods are increasingly vital in adapting large-scale pre-trained language models for diverse tasks, offering a balance between adaptability and computational efficiency.
Towards Automated Movie Trailer Generation
Movie trailers are an essential tool for promoting films and attracting audiences.
Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison
Retrieval-Augmented Neural Machine Translation (RAMT) architectures retrieve examples from memory to guide the generation process.
MaiNLP at SemEval-2024 Task 1: Analyzing Source Language Selection in Cross-Lingual Textual Relatedness
This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness (STR), on Track C: Cross-lingual.
