| Challenge: | Existing methods to constrain NMT use placeholder tags for lexicon words and hard constraints during decoding. |
| Approach: | They propose to use placeholder tags to replace lexicon words with target translations . they use a data augmentation method to make code-switched training data . |
| Outcome: | The proposed method improves translation quality without hurting unconstrained words. |
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Exploring Enhanced Code-Switched Noising for Pretraining in Neural Machine Translation (2023.findings-eacl)
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| Challenge: | Multilingual pretraining approaches to denoise synthetic code-switched data have shown that they generate the noise using non-contextual, one-to-one word translations obtained from lexicons. |
| Approach: | They propose an approach where contextual, many-to-many word translations are generated using a ‘base’ NMT model. |
| Outcome: | The proposed approach improves on 3 different language families and shows that small models can perform better than massive models like mBART50 and mRASP2 . |
CSP:Code-Switching Pre-training for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing methods to train neural machine translation models are data-hungry and low-resource . et al., 2018; Radford e.t., 2019; Yang ee.,2019) proposes a new pre-training method for NMT . |
| Approach: | They propose a new pre-training method which randomly replaces some words in the input sentence with their translation words in target language. |
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SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation (D18-1)
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| Challenge: | Existing methods for data augmentation for text-based tasks such as machine translation are limited due to noise and noise. |
| Approach: | They propose a data augmentation policy with desirable properties as an optimization problem and propose 'SwitchOut' switchout randomly replaces words in both the source and target sentences with other random words from their corresponding vocabularies. |
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Code-Switching with Word Senses for Pretraining in Neural Machine Translation (2023.findings-emnlp)
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| Challenge: | Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT) many state-of-the-art (SOTA) NMT systems struggle to handle polysemous words . |
| Approach: | They propose an end-to-end approach for pretraining multilingual NMT models leveraging word sense-specific information from Knowledge Bases. |
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Exploring Data Augmentation for Code Generation Tasks (2023.findings-eacl)
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| Challenge: | Recent advances in natural language processing have impacted how models are trained for programming language tasks. |
| Approach: | They propose to use augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. |
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Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study (2023.findings-emnlp)
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| Challenge: | Code-switching (CSW) text generation is a popular solution to address data scarcity. |
| Approach: | They compare linguistic theories, lexical replacements and back-translation approaches to Egyptian Arabic-English CSW. |
| Outcome: | The proposed methods perform best on machine translation and quality evaluation. |
Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation (P19-1)
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| Challenge: | Several configurations are tested on the DGT-TM data set for the language directions English into Dutch (ENNL) and English into Hungarian (ENHU). |
| Approach: | They propose and test two methods for augmenting NMT training data with fuzzy TM matches by concatenation. |
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Data Augmentation for Code Translation with Comparable Corpora and Multiple References (2023.findings-emnlp)
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| Challenge: | Existing methods for translating code between programming languages are limited by parallel training data. |
| Approach: | They propose a data augmentation technique that builds comparable corpora and augments existing parallel data with multiple reference translations. |
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Tagged Back-translation Revisited: Why Does It Really Work? (2020.acl-main)
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| Challenge: | In this paper, we show that neural machine translation systems trained on large back-translated data overfit some of the characteristics of machine-transcribed texts. |
| Approach: | They propose to add a tag to back-translations to help distinguish back-translated data from original parallel training data. |
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Domain Adaptation of Neural Machine Translation by Lexicon Induction (P19-1)
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| Challenge: | Neural machine translation (NMT) is sensitive to domain shift, resulting in failure for sentences with large numbers of unknown words and lack of supervision for domain-specific words. |
| Approach: | They propose an unsupervised method which fine-tunes a pre-trained out-of-domain NMT model using a pseudo-in-domain corpus. |
| Outcome: | The proposed method improves in five domains without using in-domain parallel sentences and up to 2 BLEU over strong back-translation baselines. |