Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction

• URL: http://arxiv.org/abs/2306.06804v1
• Date: Sun, 11 Jun 2023 23:27:47 GMT
• Title: Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction
• Authors: Manuel Mager, Rajat Bhatnagar, Graham Neubig, Ngoc Thang Vu, Katharina Kann
• Abstract summary: Most languages from the Americas are among them, having a limited amount of parallel and monolingual data, if any. We discuss the recent advances and findings and open questions, product of an increased interest of the NLP community in these languages.
• Score: 102.13536517783837
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural models have drastically advanced state of the art for machine translation (MT) between high-resource languages. Traditionally, these models rely on large amounts of training data, but many language pairs lack these resources. However, an important part of the languages in the world do not have this amount of data. Most languages from the Americas are among them, having a limited amount of parallel and monolingual data, if any. Here, we present an introduction to the interested reader to the basic challenges, concepts, and techniques that involve the creation of MT systems for these languages. Finally, we discuss the recent advances and findings and open questions, product of an increased interest of the NLP community in these languages.

Related papers

• 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)
• Towards a More Inclusive AI: Progress and Perspectives in Large Language Model Training for the Sámi Language [7.289015788793582]
  This work focuses on increasing technological participation for the S'ami language. We draw the attention of the ML community towards the language modeling problem of Ultra Low Resource (ULR) languages. We have compiled the available S'ami language resources from the web to create a clean dataset for training language models.
  arXiv  Detail & Related papers  (2024-05-09T13:54:22Z)
• Constructing and Expanding Low-Resource and Underrepresented Parallel Datasets for Indonesian Local Languages [0.0]
  We introduce Bhinneka Korpus, a multilingual parallel corpus featuring five Indonesian local languages. Our goal is to enhance access and utilization of these resources, extending their reach within the country.
  arXiv  Detail & Related papers  (2024-04-01T09:24:06Z)
• Extending Multilingual Machine Translation through Imitation Learning [60.15671816513614]
  Imit-MNMT treats the task as an imitation learning process, which mimicks the behavior of an expert. We show that our approach significantly improves the translation performance between the new and the original languages. We also demonstrate that our approach is capable of solving copy and off-target problems.
  arXiv  Detail & Related papers  (2023-11-14T21:04:03Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learning [7.893831644671974]
  We describe an approach exploiting bilingual and multilingual pretrained MT models to translate from Spanish to ten South American Indigenous languages. Our models set new SOTA on five out of the ten language pairs we consider, even doubling performance on one of these five pairs.
  arXiv  Detail & Related papers  (2022-05-14T07:30:03Z)
• Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning [48.15259834021655]
  We present a pragmatic approach towards building a multilingual machine translation model that covers hundreds of languages. We use a mixture of supervised and self-supervised objectives, depending on the data availability for different language pairs. We demonstrate that the synergy between these two training paradigms enables the model to produce high-quality translations in the zero-resource setting.
  arXiv  Detail & Related papers  (2022-01-09T23:36:44Z)
• Towards Zero-shot Language Modeling [90.80124496312274]
  We construct a neural model that is inductively biased towards learning human languages. We infer this distribution from a sample of typologically diverse training languages. We harness additional language-specific side information as distant supervision for held-out languages.
  arXiv  Detail & Related papers  (2021-08-06T23:49:18Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)
• When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models [2.457872341625575]
  Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. We show that such models behave in multiple ways on unseen languages.
  arXiv  Detail & Related papers  (2020-10-24T10:15:03Z)

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.