December 21, 2020 Deep Learning, NLP, Semi-supervised, Word Embeddings Leave a comment

Multilingualism in Natural Language Processing: Targeting Low Resource Indian Languages

Introduction A language is a systematic form of communication that can take a variety of forms. There are approximately 7,000 languages believed to be spoken across the globe. Despite this diversity, the majority of the world’s population speaks only a fraction of these languages. In Spite of such a rich diversity Languages are still evolving across time much like the society we live in. While the English language is uniform, having the distinct status of being the official language of […]

December 20, 2020 Deep Learning, NLP, Semi-supervised, Uncategorized, Word Embeddings Leave a comment

Multilingualism in Natural Language Processing targeting low resource Indian languages

Introduction Language is a systematic form of communication that can take a variety of forms. There are approximately 7,000 languages believed to be spoken across the globe. Despite this diversity, the majority of the world’s population speaks only a fraction of these languages. In Spite of such a rich diversity Languages are still evolving across time much like the society we live in. While the English language is uniform, having the distinct status of being the official language of multiple […]

December 10, 2020 machine translation, Neural MT, NMT, SPEECH RECOGNITION, TRANSFER LEARNING

On Knowledge Distillation for Direct Speech Translation

Direct speech translation (ST) has shown to be a complex task requiring knowledge transfer from its sub-tasks: automatic speech recognition (ASR) and machine translation (MT). For MT, one of the most promising techniques to transfer knowledge is knowledge distillation… In this paper, we compare the different solutions to distill knowledge in a sequence-to-sequence task like ST. Moreover, we analyze eventual drawbacks of this approach and how to alleviate them maintaining the benefits in terms of translation quality. (read more) PDF

November 6, 2020 Classification, Deep Learning, Intermediate, Libraries, Machine Learning, NLP, Programming, Python, Supervised, Text, Unstructured Data Leave a comment

Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python

Introduction Natural Language Processing (NLP) needs no introduction in today’s world. It’s one of the most important fields of study and research, and has seen a phenomenal rise in interest in the last decade. The basics of NLP are widely known and easy to grasp. But things start to get tricky when the text data becomes huge and unstructured. That’s where deep learning becomes so pivotal. Yes, I’m talking about deep learning for NLP tasks – a still relatively less […]

October 17, 2020 Advanced, Deep Learning, NLP, Project, Python, Supervised, Text, Unstructured Data Leave a comment

Transfer Learning for NLP: Fine-Tuning BERT for Text Classification

Introduction With the advancement in deep learning, neural network architectures like recurrent neural networks (RNN and LSTM) and convolutional neural networks (CNN) have shown a decent improvement in performance in solving several Natural Language Processing (NLP) tasks like text classification, language modeling, machine translation, etc. However, this performance of deep learning models in NLP pales in comparison to the performance of deep learning in Computer Vision. One of the main reasons for this slow progress could be the lack of […]

March 20, 2020 machine translation, NLP Leave a comment

Issue #74 – Transfer Learning for Neural Machine Translation

20 Mar20 Issue #74 – Transfer Learning for Neural Machine Translation Author: Dr. Chao-Hong Liu, Machine Translation Scientist @ Iconic Building machine translation (MT) for low-resource languages is a challenging task. This is especially true when training using neural MT (NMT) methods that require a comparatively larger corpus of parallel data. In this post, we review the work done by Zoph et al. (2016) on training NMT systems for low-resource languages using transfer learning. Transfer Learning The idea of transfer […]