NLP Made Easy

Simple code notes for explaining NLP building blocks

• Subword Segmentation Techniques

• Let's compare various tokenizers, i.e., nltk, BPE, SentencePiece, and Bert tokenizer.

• Beam Decoding

• Beam decoding is essential for seq2seq tasks. But it's notoriously complicated to implement. Here's a relatively easy one, batchfying candidates.

• How to get the last hidden vector of rnns properly

• We'll see how to get the last hidden states of Rnns in Tensorflow and PyTorch.

• Tensorflow seq2seq template based on the g2p task

• We'll write a simple template for seq2seq using Tensorflow. For demonstration, we attack the g2p task. G2p is a task of converting graphemes (spelling) to phonemes (pronunciation). It's a very good source for this purpose as it's simple enough for you to up and run.

• PyTorch seq2seq template based on the g2p task

• We'll write a simple template for seq2seq using PyTorch. For demonstration, we attack the g2p task. G2p is a task of converting graphemes (spelling) to phonemes (pronunciation). It's a very good source for this purpose as it's simple enough for you to up and run.

• [Attention mechanism](Work in progress)

• POS-tagging with BERT Fine-tuning

• BERT is known to be good at Sequence tagging tasks like Named Entity Recognition. Let's see if it's true for POS-tagging.

• Dropout in a minute

• Dropout is arguably the most popular regularization technique in deep learning. Let's check again how it work.

• Ngram LM vs. rnnlm(WIP)

• Data Augmentation for Quora Question Pairs

• Let's see if it's effective to augment training data in the task of quora question pairs.