Researchers Raise Alarms on Vulnerabilities of Machine Translation Systems
Researchers highlight the vulnerabilities to malicious attacks of multilingual public datasets and the MT systems based on them, deliberately corrupting corpora to test it.
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Machine Translation
News and analysis of the latest developments in machine translation, computer-aided-translation, natural language processing, and other language-related areas in artificial intelligence.
Huawei on Machine Translation Quality Estimation With Large Language Models
China-based researchers, including some from tech giant Huawei, publish a comprehensive overview of machine translation quality estimation.
How to Overcome the Off-Target Translation Issue in Large Language Models
A group of researchers propose a two-stage fine-tuning method to address off-target translations and boost LLMs performance in translation.
Google Translate No Longer Needs Humans to Improve Translations
The world’s leading machine translation tool no longer accepts human input through its Contribute feature citing significantly evolved systems.
Here’s a ‘Brand-New’ Massive Multilingual Dataset for Machine Translation
Researchers harness web crawls from Internet Archive and CommonCrawl to release new language resources aimed at supporting language modeling and machine translation training.
Slator Machine Translation Expert-in-the-Loop Report
60-page report on the interaction between human experts and AI in translation production, including AI-enabled workflows, adoption rates, postediting, pricing models.
Unbabel Presents a New Evaluation Metric for Chat Translation
Researchers from academia and Unbabel introduce CONTEXT-MQM, an LLM-based metric tailored for machine-translated chats. The model uses context to improve the evaluation process.
How to Train Multilingual Modular Machine Translation Systems With MAMMOTH
Researchers from the University of Helsinki, Silo AI, and NVIDIA introduce MAMMOTH, a toolkit designed to simplify the training of massively multilingual modular machine translation systems at scale.
How Google Wants to Transcend Pre-Translation in Large Language Models
Researchers from Google suggest that embracing direct inference over pre-translation enhances large language model performance and facilitates seamless multilingual communication.
Alibaba Researchers Probe Large Language Models for e-Commerce Translation
Alibaba researchers set out to test a model training and fine-tuning methodology that addresses the special characteristics of e-commerce machine translation, like keyword text.
Separating Accuracy and Fluency Improves Machine Translation Evaluation, Study Finds
Researchers highlight accuracy and fluency significance in translation. Separating the two may enhance machine translation evaluation metrics and optimize training and performance.
Meet Open-Source GenTranslate, a Speech and Text Translator Built on Meta’s SeamlessM4T
Singapore’s NTU and NVIDIA researchers introduce GenTranslate, a model using the capabilities of large language models to serve as a generative translator for speech and text.
In Machine Translation, Gold-Standard Translations Are Not Always Gold
Johns Hopkins University and Microsoft researchers underscore that gold-standard translations are not always “gold” and propose a method to enhance the performance of LLMs in machine translation.
Google AI Chief Touts Ultra Low Resource Machine Translation in Gemini 1.5 Pro Launch
The latest iteration of Google’s buzzy large language model differentiates itself with its “long-contextability” for quality assurance, automatic speech recognition, and translation.
Enter CroissantLLM, an Open-Source LLM for Efficient Translation and Easy Deployment
Academia and industry team up to launch CroissantLLM, an open-source French-English LLM that shows strong translation performance and runs well on consumer-grade local hardware.
Meet MT-Ranker, a New Machine Translation Evaluation System
Researchers from Pennsylvania State University introduce MT-Ranker, a machine translation evaluation system, which reportedly shows state-of-the-art correlation with human judgments.
How Large Language Models Fare Against ‘Classic’ Machine Translation Challenges
New research explores just how well large language models perform in “classic” machine translation challenges.
The State of AI in Literary Translation
While some literary translators warn colleagues of companies hiring post-editors for machine-translated texts, some publishers take pride in what they see as innovative workflows.
Fine-Tuned LLMs Are Good at Document-Level Machine Translation, Research Finds
Researchers from Monash University and Google demonstrate the effectiveness of fine-tuned large language models in document-level machine translation.
Amazon Flags Problem of Using Web-Scraped Machine-Translated Data in LLM Training
Amazon researchers flag the “garbage in, garbage out” problem when using low-quality, web-scraped, machine-translated data to train multilingual large language models.
Machine Translation Is More ‘Conservative’ Than Human Translation, Google Says
Researchers from Google demonstrate that machine translation is more “conservative” than human translation, with less morphosyntactic diversity, more convergent patterns, and more one-to-one alignments.
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