Machine Translation
2148 papers with code • 81 benchmarks • 76 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
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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
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.
Backdoor Attack on Multilingual Machine Translation
Our aim is to bring attention to these vulnerabilities within MNMT systems with the hope of encouraging the community to address security concerns in machine translation, especially in the context of low-resource languages.
Optical Text Recognition in Nepali and Bengali: A Transformer-based Approach
Efforts on the research and development of OCR systems for Low-Resource Languages are relatively new.
An Incomplete Loop: Deductive, Inductive, and Abductive Learning in Large Language Models
Modern language models (LMs) can learn to perform new tasks in different ways: in instruction following, the target task is described explicitly in natural language; in few-shot prompting, the task is specified implicitly with a small number of examples; in instruction inference, LMs are presented with in-context examples and are then prompted to generate a natural language task description before making predictions.
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
