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Showing result 1 - 5 of 30 essays matching the above criteria.
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1. How negation influences word order in languages : Automatic classification of word order preference in positive and negative transitive clauses
University essay from Uppsala universitet/Institutionen för lingvistik och filologiAbstract : In this work, we explore the possibility of using word alignment in parallel corpus to project language annotations such as Part-of-Speech tags and dependency relation from high-resource languages to low-resource languages. We use a parallel corpus of Bible translations, including 1,444 translations in 986 languages, and a well-developed parser is used to annotate source languages (English, French, German, and Czech). READ MORE
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2. Head-to-head Transfer Learning Comparisons made Possible : A Comparative Study of Transfer Learning Methods for Neural Machine Translation of the Baltic Languages
University essay from Uppsala universitet/Institutionen för lingvistik och filologiAbstract : The struggle of training adequate MT models using data-hungry NMT frameworks for low-resource language pairs has created a need to alleviate the scarcity of sufficiently large parallel corpora. Different transfer learning methods have been introduced as possible solutions to this problem, where a new model for a target task is initialized using parameters learned from some other high-resource task. READ MORE
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3. BERTie Bott’s Every Flavor Labels : A Tasty Guide to Developing a Semantic Role Labeling Model for Galician
University essay from Uppsala universitet/Institutionen för lingvistik och filologiAbstract : For the vast majority of languages, Natural Language Processing (NLP) tools are either absent entirely, or leave much to be desired in their final performance. Despite having nearly 4 million speakers, one such low-resource language is Galician. READ MORE
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4. Cross-Lingual and Genre-Supervised Parsing and Tagging for Low-Resource Spoken Data
University essay from Uppsala universitet/Institutionen för lingvistik och filologiAbstract : Dealing with low-resource languages is a challenging task, because of the absence of sufficient data to train machine-learning models to make predictions on these languages. One way to deal with this problem is to use data from higher-resource languages, which enables the transfer of learning from these languages to the low-resource target ones. READ MORE
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5. Low-Resource Domain Adaptation for Jihadi Discourse : Tackling Low-Resource Domain Adaptation for Neural Machine Translation Using Real and Synthetic Data
University essay from Uppsala universitet/Institutionen för lingvistik och filologiAbstract : In this thesis, I explore the problem of low-resource domain adaptation for jihadi discourse. Due to the limited availability of annotated parallel data, developing accurate and effective models in this domain poses a challenging task. READ MORE