Языки с ограниченными ресурсами

AbstractWe propose and evaluate a new paradigm for machine translation of low resource languages via the learned surface transduction and paraphrase of multilingual glosses....

While significant effort has been put into an- notating linguistic resources for several lan- guages, there are still many left that have only small amounts of such resources. This paper …...

We present a novel way of generating un- seen words, which is useful for certain ap- plications such as automatic speech recog- nition or optical character recognition in low-resource languages. …...

AbstractCurrent methods for word alignment require considerable amounts of parallel text to deliver accurate results, a requirement which is met only for a small minority of the world’s approximately 7,000 …...

AbstractAnnotation projection is a practical method to deal with the low resource problem in incident languages (IL) processing. Previous methods on annotation projection mainly relied on word alignment results without …...

In this paper we tackle a challenging name tagging problem in an emergent setting - the tagger needs to be complete within a few hours for a new incident language …...

AbstractEven for common NLP tasks, sufficient supervision is not available in many languages – morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, …...

Neural-based and phrase-based methods have shown the effectiveness and promising results in the development of current machine transla- tion. The two methods are compared on some European languages, which show …...

We present multi-speaker text-to-speech corpora for Javanese and Sundanese, the second and third largest languages of Indonesia spoken by well over a hundred million people. The key objectives were to …...

AbstractWord embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word …...

AbstractRecent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language. Here, we investigate in detail which conditions affect the performance of …...

AbstractWe work on translation from rich-resource languages to low-resource languages. The main challenges we identify are the lack of low-resource language data, effective methods for cross-lingual transfer, and the variable-binding …...

AbstractMost work on part-of-speech (POS) tagging is focused on high resource languages, or examines low-resource and active learning settings through simulated studies. We evaluate POS tagging techniques on an actual …...

Extracting semi-structured text from scientific writing in PDF files is a difficult task that researchers have faced for decades. In the 1990s, this task was largely a computer vision and …...

AbstractPrevious attempts at injecting semantic frame biases into SMT training for low resource languages failed because either (a) no semantic parser is available for the low resource input language; or …...

Representing words as vectors which encode their semantic properties is an important component in natural language processing. Recent advances in distributional semantics have led to the rise of neural network-based …...

AbstractWe show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no pre-training, fastText, ELMo, …...

AbstractText classification tends to be difficult when data are inadequate considering the amount of manually labeled text corpora. For low-resource agglutinative languages including Uyghur, Kazakh, and Kyrgyz (UKK languages), in …...

AbstractLanguage recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited …...

AbstractReinforcement learning (RL) has made remarkable progress in neural machine translation (NMT). However, it exists the problems with uneven sampling distribution, sparse rewards and high variance in training phase. Therefore, …...

AbstractWe propose the task of unsupervised morphological paradigm completion. Given only raw text and a lemma list, the task consists of generating the morphological paradigms, i.e., all inflected forms, of …...

While word segmentation is a solved prob- lem in many languages, it is still a chal- lenge in continuous-script or low-resource lan- guages. Like other NLP tasks, word segmen- tation …...

AbstractNamed Entity Recognition (NER) for low-resource languages is a both practical and challenging research problem. This paper addresses zero-shot transfer for cross-lingual NER, especially when the amount of source-language training …...

AbstractIn structured prediction problems, cross-lingual transfer learning is an efficient way to train quality models for low-resource languages, and further improvement can be obtained by learning from multiple source languages. …...

AbstractMassively multilingual language models such as multilingual BERT offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. However, due to limited capacity and large differences in pretraining data …...

Despite the recent advancement in NLP re- search, cross-lingual transfer for natural lan- guage generation is relatively understudied. In this work, we transfer supervision from high resource language (HRL) to …...

AbstractRecent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many …...

AbstractThis paper provides an overview of the main achievements of the completed PRINCIPLE project, a 2-year action funded by the European Commission under the Connecting Europe Facility (CEF) programme. PRINCIPLE …...

AbstractWhile multilingual training is now an essential ingredient in machine translation (MT) systems, recent work has demonstrated that it has different effects in different multilingual settings, such as many-to-one, one-to-many, …...

AbstractThe long-standing endeavor of relating the textual and the visual domain recently underwent a pivotal breakthrough, as OpenAI released CLIP. This model distinguishes how well an English text corresponds with …...

Most of the available resources for low resource languages are crawled from the web. In order to obtain reasonable ma- chine translation performance with such datasets, it is important to …...

AbstractAn increasing number of papers have been addressing issues related to low-resource languages and the transcription bottleneck paradigm. After several years spent in Northern Australia, where some of the strongest …...

AbstractThe surging demand for multilingual dialogue systems often requires a costly labeling process for each language addition. For low resource languages, human annotators are continuously tasked with the adaptation of …...

AbstractAlmost all summarisation methods and datasets focus on a single language and short summaries. We introduce a new dataset called WikinewsSum for English, German, French, Spanish, Portuguese, Polish, and Italian …...

AbstractBodo is a scheduled Indian language spoken largely by the Bodo community of Assam and other northeastern Indian states. Due to a lack of resources, it is difficult for young …...

AbstractThis paper considers the task of parsing low-resource languages in a scenario where parallel English data and also a limited seed of annotated sentences in the target language are available, …...

AbstractAutomatic morphological processing can aid downstream natural language processing applications, especially for low-resource languages, and assist language documentation efforts for endangered languages. Having long been multilingual, the field of computational …...

AbstractPre-trained multilingual models, such as mBERT, XLM-R and mT5, are used to improve the performance on various tasks in low-resource languages via cross-lingual transfer. In this framework, English is usually …...

AbstractIn recent years, the natural language processing (NLP) community has given increased attention to the disparity of efforts directed towards high-resource languages over low-resource ones. Efforts to remedy this delta …...

AbstractMultilingual language models have shown impressive cross-lingual transfer ability across a diverse set of languages and tasks. To improve the cross-lingual ability of these models, some strategies include transliteration and …...

AbstractNatural language processing (NLP) has a significant impact on society via technologies such as machine translation and search engines. Despite its success, NLP technology is only widely available for high-resource …...

AbstractAlthough unsupervised neural machine translation (UNMT) has achieved success in many language pairs, the copying problem, i.e., directly copying some parts of the input sentence as the translation, is common …...

AbstractRecently, the internet has emerged as the primary platform for accessing news. In the majority of these news platforms, the users now have the ability to post comments on news …...

AbstractThe main goal of this project is to explore the techniques for training NMT systems applied to Spanish, Portuguese, Catalan, Galician, Asturian, Aragonese and Aranese. These languages belong to the …...