| Challenge: | Experimental results show that multilingual NMT models handle multiple language pairs in one model. |
| Approach: | They propose an interactive approach to translate a source language into two different languages simultaneously and interactively. |
| Outcome: | The proposed approach improves on IWSLT and WMT datasets. |
Similar Papers
Multilingual Simultaneous Neural Machine Translation (2021.findings-acl)
Copied to clipboard
| Challenge: | Simultaneous machine translation (SIMT) involves translating source utterances to the target language in real-time before the speaker utterrance completes. |
| Approach: | They propose a multilingual approach to simultaneous machine translation where a single model simultaneously translates between multiple languages. |
| Outcome: | The proposed multilingual approach improves on two Germanic and three Romance languages and is on-par or better than the universal model trained for all languages. |
Improving Multilingual Neural Machine Translation with Auxiliary Source Languages (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Prior work has shown that translating from multiple source languages improves translation quality. |
| Approach: | They propose to exploit multiple source sentences from auxiliary languages to improve multilingual translation in a more common scenario by using synthetic multi-source corpora. |
| Outcome: | Extensive experiments on Chinese/English-Japanese and a large-scale multilingual translation benchmark show that the proposed model outperforms the baseline model significantly by +4.0 BLEU. |
On-the-Fly Fusion of Large Language Models and Machine Translation (2024.findings-naacl)
Copied to clipboard
| Challenge: | a weaker-at-translation LLM can improve translations of a NMT model, compared to a strong dedicated model. |
| Approach: | They propose to ensemble a neural machine translation model with a large language model, prompted on the same task and input. |
| Outcome: | The proposed method can be combined with various techniques from LLM prompting, such as in context learning and translation context. |
Multilingual Neural Machine Translation (2020.coling-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
| Approach: | They will cover the latest advances in NMT approaches that leverage multilingualism . they will focus on topics such as language divergence, transfer learning and pivoting . |
| Outcome: | This tutorial will cover the latest advances in NMT to enhance low-resource translation models. |
Simultaneous Translation (2020.emnlp-tutorials)
Copied to clipboard
| Challenge: | Simultaneous translation is a problem that has long been considered one of the hardest problems in AI . this tutorial will provide a deep understanding of the history and the recent advances in simultaneous translation. |
| Approach: | This tutorial will examine the design and evaluation of policies for simultaneous translation . it will provide an overview of the history and recent advances in simultaneous translation. |
| Outcome: | This tutorial will examine the design and evaluation of policies for simultaneous translation . |
Multilingual Unsupervised Neural Machine Translation with Denoising Adapters (2021.emnlp-main)
Copied to clipboard
| Challenge: | Multilingual unsupervised machine translation is a computationally expensive and hard to tune approach . auxiliary parallel data is used to train translation systems from monolingual data . |
| Approach: | They propose to use auxiliary parallel language pairs to train unsupervised machine translations . they propose to add auxiliary languages to pre-trained mBART-50 models with denoising adapters . |
| Outcome: | The proposed approach is on-par with back-translation and allows adding unseen languages incrementally. |
A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to train neural network-based models for code-mixing are limited due to language specificity of code-mixed text. |
| Approach: | They propose a deep learning approach to generate code-mixed text from English to multiple languages without any parallel data. |
| Outcome: | The proposed approach generates a code-mixed text from English to multiple languages without any parallel data. |
Exploiting Monolingual Data at Scale for Neural Machine Translation (D19-1)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) is a well-known and expensive task. |
| Approach: | They propose a method to use target-side monolingual data for neural machine translation and propose 'synthetic bitext' they propose generating synthetic bitext by translating monolingual into the other domain using models pretrained on genuine bitext. |
| Outcome: | The proposed approach achieves state-of-the-art results on WMT16, WMT17, WTM18 EnglishGerman translations and WTM19 GermanFrench translations. |
Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders (2021.eacl-main)
Copied to clipboard
| Challenge: | State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages. |
| Approach: | They propose an encoder-decoder approach that can be extended to new languages by learning their corresponding modules. |
| Outcome: | The proposed approach outperforms the universal encoder-decoder by 3.28 BLEU points on average while allowing to add new languages without retraining the rest of the modules. |
Language-aware Interlingua for Multilingual Neural Machine Translation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing multilingual neural machine translation models fail to capture diversity and specificity of different languages, resulting in inferior performance against individual models that are sufficiently trained. |
| Approach: | They propose to integrate a language-aware interlingua into an Encoder-Decoder architecture to learn a semantic representation from the semantic spaces of different languages while allowing for language-specific specialization of a particular language pair. |
| Outcome: | The proposed model achieves remarkable improvements over state-of-the-art multilingual NMT models and produces comparable performance with strong individual models. |