FAME-MT Dataset: Formality Awareness Made Easy for Machine Translation Purposes
- URL: http://arxiv.org/abs/2405.11942v1
- Date: Mon, 20 May 2024 10:35:30 GMT
- Title: FAME-MT Dataset: Formality Awareness Made Easy for Machine Translation Purposes
- Abstract summary: We present FAME-MT -- a dataset consisting of 11.2 million translations between 15 European source languages and 8 European target languages.
This dataset can be used to fine-tune machine translation models to ensure a given formality level for each European target language.
We present a publicly available proof-of-concept machine translation model that uses the dataset to steer the formality level of the translation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: People use language for various purposes. Apart from sharing information, individuals may use it to express emotions or to show respect for another person. In this paper, we focus on the formality level of machine-generated translations and present FAME-MT -- a dataset consisting of 11.2 million translations between 15 European source languages and 8 European target languages classified to formal and informal classes according to target sentence formality. This dataset can be used to fine-tune machine translation models to ensure a given formality level for each European target language considered. We describe the dataset creation procedure, the analysis of the dataset's quality showing that FAME-MT is a reliable source of language register information, and we present a publicly available proof-of-concept machine translation model that uses the dataset to steer the formality level of the translation. Currently, it is the largest dataset of formality annotations, with examples expressed in 112 European language pairs. The dataset is published online: https://github.com/laniqo-public/fame-mt/ .
Related papers
- Machine Translation to Control Formality Features in the Target Language [0.9208007322096532]
This research explores how machine learning methods are used to translate from English to languages with formality.
It was done by training a bilingual model in a formality-controlled setting and comparing its performance with a pre-trained multilingual model.
We evaluate the official formality accuracy(ACC) by comparing the predicted masked tokens with the ground truth.
arXiv Detail & Related papers (2023-11-22T15:42:51Z) - Unified Model Learning for Various Neural Machine Translation [63.320005222549646]
Existing machine translation (NMT) studies mainly focus on developing dataset-specific models.
We propose a versatile'' model, i.e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks.
OurNMT results in substantial improvements over dataset-specific models with significantly reduced model deployment costs.
arXiv Detail & Related papers (2023-05-04T12:21:52Z) - Statistical Machine Translation for Indic Languages [1.8899300124593648]
This paper canvasses about the development of bilingual Statistical Machine Translation models.
To create the system, MOSES open-source SMT toolkit is explored.
In our experiment, the quality of the translation is evaluated using standard metrics such as BLEU, METEOR, and RIBES.
arXiv Detail & Related papers (2023-01-02T06:23:12Z) - Building Machine Translation Systems for the Next Thousand Languages [102.24310122155073]
We describe results in three research domains: building clean, web-mined datasets for 1500+ languages, developing practical MT models for under-served languages, and studying the limitations of evaluation metrics for these languages.
We hope that our work provides useful insights to practitioners working towards building MT systems for currently understudied languages, and highlights research directions that can complement the weaknesses of massively multilingual models in data-sparse settings.
arXiv Detail & Related papers (2022-05-09T00:24:13Z) - Detecting Text Formality: A Study of Text Classification Approaches [78.11745751651708]
This work proposes the first to our knowledge systematic study of formality detection methods based on statistical, neural-based, and Transformer-based machine learning methods.
We conducted three types of experiments -- monolingual, multilingual, and cross-lingual.
The study shows the overcome of Char BiLSTM model over Transformer-based ones for the monolingual and multilingual formality classification task.
arXiv Detail & Related papers (2022-04-19T16:23:07Z) - Beyond English-Centric Multilingual Machine Translation [74.21727842163068]
We create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages.
We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining.
Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT.
arXiv Detail & Related papers (2020-10-21T17:01:23Z) - The Tatoeba Translation Challenge -- Realistic Data Sets for Low
Resource and Multilingual MT [0.0]
This paper describes the development of a new benchmark for machine translation that provides training and test data for thousands of language pairs.
The main goal is to trigger the development of open translation tools and models with a much broader coverage of the World's languages.
arXiv Detail & Related papers (2020-10-13T13:12:21Z) - MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset [49.602565530704005]
The dataset contains eleven language pairs, with human labels for up to 10,000 translations per language pair.
It also contains the post-edited sentences, as well as titles of the articles where the sentences were extracted from, and the neural MT models used to translate the text.
arXiv Detail & Related papers (2020-10-09T10:12:02Z) - A High-Quality Multilingual Dataset for Structured Documentation
Translation [101.41835967142521]
This paper presents a high-quality multilingual dataset for the documentation domain.
We collect XML-structured parallel text segments from the online documentation for an enterprise software platform.
arXiv Detail & Related papers (2020-06-24T02:08:44Z) - Data and Representation for Turkish Natural Language Inference [6.135815931215188]
We offer a positive response for natural language inference (NLI) in Turkish.
We translate two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels.
We find that in-language embeddings are essential and that morphological parsing can be avoided where the training set is large.
arXiv Detail & Related papers (2020-04-30T17:12:52Z)
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.