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Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara
Low-resource languages present unique challenges to (neural) machine tra...
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Urdu-English Machine Transliteration using Neural Networks
Machine translation has gained much attention in recent years. It is a s...
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Neural Machine Translation based Word Transduction Mechanisms for Low-Resource Languages
Out-Of-Vocabulary (OOV) words can pose serious challenges for machine tr...
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Low-Resource Contextual Topic Identification on Speech
In topic identification (topic ID) on real-world unstructured audio, an ...
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Deriving Machine Attention from Human Rationales
Attention-based models are successful when trained on large amounts of d...
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FFR V1.0: Fon-French Neural Machine Translation
Africa has the highest linguistic diversity in the world. On account of ...
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Pseudolikelihood Reranking with Masked Language Models
We rerank with scores from pretrained masked language models like BERT t...
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Context Models for OOV Word Translation in Low-Resource Languages
Out-of-vocabulary word translation is a major problem for the translation of low-resource languages that suffer from a lack of parallel training data. This paper evaluates the contributions of target-language context models towards the translation of OOV words, specifically in those cases where OOV translations are derived from external knowledge sources, such as dictionaries. We develop both neural and non-neural context models and evaluate them within both phrase-based and self-attention based neural machine translation systems. Our results show that neural language models that integrate additional context beyond the current sentence are the most effective in disambiguating possible OOV word translations. We present an efficient second-pass lattice-rescoring method for wide-context neural language models and demonstrate performance improvements over state-of-the-art self-attention based neural MT systems in five out of six low-resource language pairs.
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