Unsupervised Neural Machine Translation (UNMT) approaches have gained widespread popularity in recent times. Though these approaches show impressive translation performance using only monolingual corpora of the languages involved, these approaches …
Transfer learning approaches for Neural Machine Translation (NMT) train a NMT model on the assisting-target language pair (parent model) which is later fine-tuned for the source-target language pair of interest (child model), with the target language …
Existing supervised solutions for Named Entity Recognition (NER) typically rely on a large annotated corpus. Collecting large amounts of NER annotated corpus is time-consuming and requires considerable human effort. However, collecting small amounts …
Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly training a neural network for multiple languages. Typically, the goal is improving the NER performance of one of the languages (the primary language) using the other …