Generative-Adversarial Networks for Low-Resource Language Data Augmentation in Machine Translation

• URL: http://arxiv.org/abs/2409.00071v1
• Date: Sat, 24 Aug 2024 00:02:00 GMT
• Title: Generative-Adversarial Networks for Low-Resource Language Data Augmentation in Machine Translation
• Authors: Linda Zeng,
• Abstract summary: We propose utilizing a generative-adrial network (GAN) to augment low-resource language data. Our model shows potential at data augmentation, generating monolingual language data with sentences such as "ask me that healthy lunch im cooking up"
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural Machine Translation (NMT) systems struggle when translating to and from low-resource languages, which lack large-scale data corpora for models to use for training. As manual data curation is expensive and time-consuming, we propose utilizing a generative-adversarial network (GAN) to augment low-resource language data. When training on a very small amount of language data (under 20,000 sentences) in a simulated low-resource setting, our model shows potential at data augmentation, generating monolingual language data with sentences such as "ask me that healthy lunch im cooking up," and "my grandfather work harder than your grandfather before." Our novel data augmentation approach takes the first step in investigating the capability of GANs in low-resource NMT, and our results suggest that there is promise for future extension of GANs to low-resource NMT.

Related papers

• Enhancing Code Generation for Low-Resource Languages: No Silver Bullet [55.39571645315926]
  Large Language Models (LLMs) rely on large and diverse datasets to learn syntax, semantics, and usage patterns of programming languages. For low-resource languages, the limited availability of such data hampers the models' ability to generalize effectively. We present an empirical study investigating the effectiveness of several approaches for boosting LLMs' performance on low-resource languages.
  arXiv  Detail & Related papers  (2025-01-31T12:23:28Z)
• Efficient Continual Pre-training of LLMs for Low-resource Languages [45.44796295841526]
  We develop a new algorithm to select a subset of texts from a larger corpus. In search of further improvement, we design a new algorithm to select tokens to include in the LLM vocabulary.
  arXiv  Detail & Related papers  (2024-12-13T16:13:35Z)
• A multilingual training strategy for low resource Text to Speech [5.109810774427171]
  We investigate whether data from social media can be used for a small TTS dataset construction, and whether cross lingual transfer learning can work with this type of data. To do so, we explore how data from foreign languages may be selected and pooled to train a TTS model for a target low resource language. Our findings show that multilingual pre-training is better than monolingual pre-training at increasing the intelligibility and naturalness of the generated speech.
  arXiv  Detail & Related papers  (2024-09-02T12:53:01Z)
• High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering [1.8843687952462742]
  This paper proposes a novel way of utilizing a monolingual corpus on the source side to assist Neural Machine Translation (NMT) in low-resource settings. We realize this concept by employing a Generative Adversarial Network (GAN), which augments the training data for the discriminator while mitigating the interference of low-quality synthetic monolingual translations with the generator.
  arXiv  Detail & Related papers  (2024-08-22T02:35:47Z)
• Reduce, Reuse, Recycle: Is Perturbed Data better than Other Language augmentation for Low Resource Self-Supervised Speech Models [48.44820587495038]
  Self-supervised representation learning (SSRL) has demonstrated superior performance than supervised models for tasks including phoneme recognition. Training SSRL models poses a challenge for low-resource languages where sufficient pre-training data may not be available. We propose to use audio augmentation techniques, namely: pitch variation, noise addition, accented target language and other language speech to pre-train SSRL models in a low resource condition and evaluate phoneme recognition.
  arXiv  Detail & Related papers  (2023-09-22T10:09:09Z)
• Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili [1.9686054517684888]
  In machine translation, majority of the language pairs around the world are considered low resource because they have little parallel data available. We study and apply three simple data augmentation techniques popularly used in text classification tasks. We see that there is potential to use these methods in neural machine translation when more extensive experiments are done with diverse datasets.
  arXiv  Detail & Related papers  (2023-06-12T20:43:24Z)
• Improving Cross-lingual Information Retrieval on Low-Resource Languages via Optimal Transport Distillation [21.057178077747754]
  In this work, we propose OPTICAL: Optimal Transport distillation for low-resource Cross-lingual information retrieval. By separating the cross-lingual knowledge from knowledge of query document matching, OPTICAL only needs bitext data for distillation training. Experimental results show that, with minimal training data, OPTICAL significantly outperforms strong baselines on low-resource languages.
  arXiv  Detail & Related papers  (2023-01-29T22:30:36Z)
• Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation [50.54059385277964]
  We present a novel data augmentation paradigm termed Continuous Semantic Augmentation (CsaNMT) CsaNMT augments each training instance with an adjacency region that could cover adequate variants of literal expression under the same meaning.
  arXiv  Detail & Related papers  (2022-04-14T08:16:28Z)
• Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT [129.99918589405675]
  We present an effective approach that reuses an LM that is pretrained only on the high-resource language. The monolingual LM is fine-tuned on both languages and is then used to initialize a UNMT model. Our approach, RE-LM, outperforms a competitive cross-lingual pretraining model (XLM) in English-Macedonian (En-Mk) and English-Albanian (En-Sq)
  arXiv  Detail & Related papers  (2020-09-16T11:37:10Z)
• Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation [54.52971020087777]
  Using monolingual data significantly boosts the translation quality of low-resource languages in multilingual models. Self-supervision improves zero-shot translation quality in multilingual models. We get up to 33 BLEU on ro-en translation without any parallel data or back-translation.
  arXiv  Detail & Related papers  (2020-05-11T00:20:33Z)
• Pre-training via Leveraging Assisting Languages and Data Selection for Neural Machine Translation [49.51278300110449]
  We propose to exploit monolingual corpora of other languages to complement the scarcity of monolingual corpora for the languages of interest. A case study of low-resource Japanese-English neural machine translation (NMT) reveals that leveraging large Chinese and French monolingual corpora can help overcome the shortage of Japanese and English monolingual corpora.
  arXiv  Detail & Related papers  (2020-01-23T02:47:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.