Distributed Representations of Words and Phrases and their Compositionality

graykode/nlp-tutorial • • NeurIPS 2013

Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.

Attention Is All You Need

graykode/nlp-tutorial • • NeurIPS 2017

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.

Abstractive Text Summarization Coreference Resolution +8

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

graykode/nlp-tutorial • • NAACL 2019

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.

Citation Intent Classification Common Sense Reasoning +16

Neural Machine Translation by Jointly Learning to Align and Translate

graykode/nlp-tutorial • • 1 Sep 2014

Neural machine translation is a recently proposed approach to machine translation.

Bangla Spelling Error Correction Dialogue Generation +3

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

graykode/nlp-tutorial • • 3 Jun 2014

In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN).

Machine Translation Translation

Bag of Tricks for Efficient Text Classification

graykode/nlp-tutorial • • EACL 2017

This paper explores a simple and efficient baseline for text classification.

Emotion Recognition in Conversation General Classification +2

Predicting Subjective Features of Questions of QA Websites using BERT

graykode/nlp-tutorial • • ICWR 2020

Community Question-Answering websites, such as StackOverflow and Quora, expect users to follow specific guidelines in order to maintain content quality.

Community Question Answering

Efficient Estimation of Word Representations in Vector Space

graykode/nlp-tutorial • • 16 Jan 2013

We propose two novel model architectures for computing continuous vector representations of words from very large data sets.

Word Similarity