Papers by Ehsan Zare Borzeshi
BiLSTM-CRF for Persian Named-Entity Recognition ArmanPersoNERCorpus: the First Entity-Annotated Persian Dataset (L18-1)
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| Challenge: | Named-entity recognition (NER) is a natural language processing component that aims to identify all the "named entities" (NEs) in an unstructured text. |
| Approach: | They propose a deep learning approach for name-entity recognition in Persian . they publicize an entity-annotated Persian dataset and train word embeddings . |
| Outcome: | The proposed approach achieves a 77.45% CoNLL F 1 score for Persian NER based on a deep learning architecture and pre-trained word embeddings. |
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems (N19-1)
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| Challenge: | Existing methods to regularize neural machine translation are limited in low-resource settings. |
| Approach: | They propose a method that uses regressing word embeddings to regularize neural machine translation. |
| Outcome: | The proposed system improves on a strong baseline and a state-of-the-art system. |
English-Basque Statistical and Neural Machine Translation (L18-1)
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| Challenge: | Neural machine translation (NMT) requires large training corpora, which is problematic for low-resource languages. |
| Approach: | They propose to use an open-domain and an IT-domain corpora to train machine translations in English-Basque. |
| Outcome: | The proposed systems outperform OpenNMT, Moses SMT and Google Translate in English-Basque translation. |