| Challenge: | a growing demand for translations and multilingual content is surpassing the supply of professional translation services. |
| Approach: | They present a custom machine translation platform called Tilde MT that provides linguistic data storage, data cleaning and normalisation, statistical and neural machine translation system training and hosting functionality. |
| Outcome: | The proposed platform provides linguistic data storage, data cleaning and normalisation, statistical and neural machine translation system training and hosting functionality, and wide integration capabilities. |
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| Challenge: | Recent advances in machine translation and natural language generation have created many challenges in this field especially when context is considered. |
| Approach: | They propose to leverage data from machine translation and natural language generation tasks to do transfer learning between MT, NLG and MT with source-side metadata. |
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Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System (N18-3)
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| Challenge: | In recent years, there has been growing interest in voice-controlled devices, such as Amazon Alexa or Google home. |
| Approach: | They investigate the use of Machine Translation to bootstrap a natural language understanding system for a new language for the use case of a large-scale voice-controlled device. |
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TRANSLATIONCORRECT: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance (2025.acl-demo)
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| Challenge: | Current workflows for machine translation (MT) post-editing and research data collection are inefficient and time-consuming. |
| Approach: | They propose a framework that combines MT and error prediction within a single environment. |
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MT-Telescope: An interactive platform for contrastive evaluation of MT systems (2021.acl-demo)
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| Challenge: | MT-Telescope is an open source, written in Python, and is built around a user friendly and dynamic web interface. |
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nmT5 - Is parallel data still relevant for pre-training massively multilingual language models? (2021.acl-short)
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| Challenge: | Recent studies have shown that cross-lingual transfer learning in pre-trained multilingual models could be improved further by incorporating parallel data. |
| Approach: | They propose to integrate parallel data into mT5 pre-training to improve results on downstream multilingual and cross-lingual tasks. |
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mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer (2021.naacl-main)
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Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel
| Challenge: | Current natural language processing pipelines often use transfer learning, where a model is pre-trained on a data-rich task before being fine-tuned on . this significantly limits their use given that roughly 80% of the world population does not speak English. |
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Revisiting Machine Translation for Cross-lingual Classification (2023.emnlp-main)
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| Challenge: | Recent work in cross-lingual learning has pivoted around multilingual models, which are typically pretrained on unlabeled corpora in multiple languages using some form of language modeling objective. |
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MTNT: A Testbed for Machine Translation of Noisy Text (D18-1)
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| Challenge: | Noisy input text can cause disastrous mistranslations in most modern machine translation systems. |
| Approach: | They propose a benchmark dataset for Machine Translation of Noisy Text (MTNT) they use reddit comments and professionally sourced translations to examine noise types. |
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Very Large-Scale Lexical Resources to Enhance Chinese and Japanese Machine Translation (L18-1)
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| Challenge: | A major issue in machine translation applications is the recognition and translation of named entities. |
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Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages (2024.naacl-long)
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Nathaniel Robinson, Raj Dabre, Ammon Shurtz, Rasul Dent, Onenamiyi Onesi, Claire Monroc, Loïc Grobol, Hasan Muhammad, Ashi Garg, Naome Etori, Vijay Murari Tiyyala, Olanrewaju Samuel, Matthew Stutzman, Bismarck Odoom, Sanjeev Khudanpur, Stephen Richardson, Kenton Murray
| Challenge: | Creole languages are used in much of Latin America, Africa and the Caribbean . a large multilingual bitext like ours has potential to build the best yet or first ever MT models for many languages . |
| Approach: | They present the largest cumulative dataset to date for Creole language MT . they provide MT models supporting all 41 Creoles in 172 translation directions . |
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