23 research outputs found
Building the tatar-Russian NMT system based on re-translation of multilingual data
© Springer Nature Switzerland AG 2018. This paper assesses the possibility of combining the rule-based and the neural network approaches to the construction of the machine translation system for the Tatar-Russian language pair. We propose a rule-based system that allows using parallel data of a group of 6 Turkic languages (Tatar, Kazakh, Kyrgyz, Crimean-Tatar, Uzbek, Turkish) and the Russian language to overcome the problem of limited Tatar-Russian data. We incorporated modern approaches for data augmentation, neural networks training and linguistically motivated rule-based methods. The main results of the work are the creation of the first neural Tatar-Russian translation system and the improvement of the translation quality in this language pair in terms of BLEU scores from 12 to 39 and from 17 to 45 for both translation directions (comparing to the existing translation system). Also the translation between any of the Tatar, Kazakh, Kyrgyz, Crimean Tatar, Uzbek, Turkish languages becomes possible, which allows to translate from all of these Turkic languages into Russian using Tatar as an intermediate language
Evaluating Multiway Multilingual NMT in the Turkic Languages
Despite the increasing number of large and comprehensive machine translation (MT) systems, evaluation of these methods in various languages has been restrained by the lack of high-quality parallel corpora as well as engagement with the people that speak these languages. In this study, we present an evaluation of state-of-the-art approaches to training and evaluating MT systems in 22 languages from the Turkic language family, most of which being extremely under-explored. First, we adopt the TIL Corpus with a few key improvements to the training and the evaluation sets. Then, we train 26 bilingual baselines as well as a multi-way neural MT (MNMT) model using the corpus and perform an extensive analysis using automatic metrics as well as human evaluations. We find that the MNMT model outperforms almost all bilingual baselines in the out-of-domain test sets and finetuning the model on a downstream task of a single pair also results in a huge performance boost in both low- and high-resource scenarios. Our attentive analysis of evaluation criteria for MT models in Turkic languages also points to the necessity for further research in this direction. We release the corpus splits, test sets as well as models to the public.Peer reviewe
A Large-Scale Study of Machine Translation in Turkic Languages
Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems. However, there is still a large number of languages that are yet to reap the benefits of NMT. In this paper, we provide the first large-scale case study of the practical application of MT in the Turkic language family in order to realize the gains of NMT for Turkic languages under high-resource to extremely low-resource scenarios. In addition to presenting an extensive analysis that identifies the bottlenecks towards building competitive systems to ameliorate data scarcity, our study has several key contributions, including, i) a large parallel corpus covering 22 Turkic languages consisting of common public datasets in combination with new datasets of approximately 1.4 million parallel sentences, ii) bilingual baselines for 26 language pairs, iii) novel high-quality test sets in three different translation domains and iv) human evaluation scores. All models, scripts, and data will be released to the public.Peer reviewe
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
We introduce an architecture to learn joint multilingual sentence
representations for 93 languages, belonging to more than 30 different language
families and written in 28 different scripts. Our system uses a single BiLSTM
encoder with a shared BPE vocabulary for all languages, which is coupled with
an auxiliary decoder and trained on publicly available parallel corpora. This
enables us to learn a classifier on top of the resulting sentence embeddings
using English annotated data only, and transfer it to any of the 93 languages
without any modification. Our approach sets a new state-of-the-art on zero-shot
cross-lingual natural language inference for all the 14 languages in the XNLI
dataset but one. We also achieve very competitive results in cross-lingual
document classification (MLDoc dataset). Our sentence embeddings are also
strong at parallel corpus mining, establishing a new state-of-the-art in the
BUCC shared task for 3 of its 4 language pairs. Finally, we introduce a new
test set of aligned sentences in 122 languages based on the Tatoeba corpus, and
show that our sentence embeddings obtain strong results in multilingual
similarity search even for low-resource languages. Our PyTorch implementation,
pre-trained encoder and the multilingual test set will be freely available
Lego-MT: Towards Detachable Models in Massively Multilingual Machine Translation
Multilingual neural machine translation (MNMT) aims to build a unified model
for many language directions. Existing monolithic models for MNMT encounter two
challenges: parameter interference among languages and inefficient inference
for large models. In this paper, we revisit the classic multi-way structures
and develop a detachable model by assigning each language (or group of
languages) to an individual branch that supports plug-and-play training and
inference. To address the needs of learning representations for all languages
in a unified space, we propose a novel efficient training recipe, upon which we
build an effective detachable model, Lego-MT. For a fair comparison, we collect
data from OPUS and build a translation benchmark covering 433 languages and
1.3B parallel data. Experiments show that Lego-MT with 1.2B parameters brings
an average gain of 3.2 spBLEU. It even outperforms M2M-100 with 12B parameters.
The proposed training recipe brings a 28.2 speedup over the
conventional multi-way training method.\footnote{
\url{https://github.com/CONE-MT/Lego-MT}.}Comment: ACL 2023 Finding