Neural Machine Translation with Attention (Dynet)

Transition-based joint syntactic dependency parser and semantic role labeler using a stack LSTM RNN architecture.

BiLSTM-CRF for sequence labeling in Dynet

DyNet implementation of stack LSTM experiments by Grefenstette et al.

Selective Encoding for Abstractive Sentence Summarization in DyNet

Deep Recurrent Generative Decoder for Abstractive Text Summarization in DyNet

Convolutional Neural Networks for Sentence Classification in DyNet

DyNet: The Dynamic Neural Network Toolkit

Code that exemplifies neural network solutions for classification tasks with DyNet. On top of that, the code demonstrates how to implement a custom classifier that is compatible with scikit-learn's API.

Haskell binding for DyNet

DyNet interfaces for Humans

Simple tutorials for dynet

A Neural Attention Model for Abstractive Sentence Summarization in DyNet

A neural conditional random field implemented in DyNet.

Source code for the paper "Morphological Inflection Generation with Hard Monotonic Attention"

See http://github.com/onurgu/joint-ner-and-md-tagger This repository is basically a Bi-LSTM based sequence tagger in both Tensorflow and Dynet which can utilize several sources of information about each word unit like word embeddings, character based embeddings and morphological tags from an FST to obtain the representation for that specific wor…

a simple modification of Chris Dyer's stack LSTM Parser

Code for paper "End-to-End Reinforcement Learning for Automatic Taxonomy Induction", ACL 2018