Displaying 1 to 15 from 15 results

OpenNMT - Open Source Neural Machine Translation in Torch

  •    Lua

OpenNMT is a full-featured, open-source (MIT) neural machine translation system utilizing the Torch mathematical toolkit. OpenNMT only requires a Torch installation with few dependencies.

OpenNMT-py - Open Source Neural Machine Translation in PyTorch

  •    Python

This is a Pytorch port of OpenNMT, an open-source (MIT) neural machine translation system. It is designed to be research friendly to try out new ideas in translation, summary, image-to-text, morphology, and many other domains. Codebase is relatively stable, but PyTorch is still evolving. We currently only support PyTorch 0.4 and recommend forking if you need to have stable code.

sentencepiece - Unsupervised text tokenizer for Neural Network-based text generation.

  •    C++

SentencePiece is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation systems where the vocabulary size is predetermined prior to the neural model training. SentencePiece implements sub-word units (also known as wordpieces [Wu et al.] [Schuster et al.] and byte-pair-encoding (BPE) [Sennrich et al.]) with the extension of direct training from raw sentences. SentencePiece allows us to make a purely end-to-end system that does not depend on language-specific pre/postprocessing.This is not an official Google product.




sockeye - Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet

  •    Python

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton and Matt Post (2017): Sockeye: A Toolkit for Neural Machine Translation. In eprint arXiv:cs-CL/1712.05690.If you are interested in collaborating or have any questions, please submit a pull request or issue. You can also send questions to sockeye-dev-at-amazon-dot-com.

OpenNMT-tf - Neural machine translation and sequence learning using TensorFlow

  •    Python

and all of the above can be used simultaneously to train novel and complex architectures. See the predefined models to discover how they are defined and the API documentation to customize them. Additional experimental models are available in the config/models/ directory and can be used with the option --model <model_file.py>.

neuralmonkey - An open-source tool for sequence learning in NLP built on TensorFlow.

  •    Python

The Neural Monkey package provides a higher level abstraction for sequential neural network models, most prominently in Natural Language Processing (NLP). It is built on TensorFlow. It can be used for fast prototyping of sequential models in NLP which can be used e.g. for neural machine translation or sentence classification. The higher-level API brings together a collection of standard building blocks (RNN encoder and decoder, multi-layer perceptron) and a simple way of adding new building blocks implemented directly in TensorFlow.

OpenSeq2Seq - Toolkit for efficient experimentation with various sequence-to-sequence models

  •    Python

This is a research project, not an official NVIDIA product. OpenSeq2Seq main goal is to allow researchers to most effectively explore various sequence-to-sequence models. The efficiency is achieved by fully supporting distributed and mixed-precision training. OpenSeq2Seq is built using TensorFlow and provides all the necessary building blocks for training encoder-decoder models for neural machine translation and automatic speech recognition. We plan to extend it with other modalities in the future.


tensorflow-shakespeare - Neural machine translation between the writings of Shakespeare and modern English using TensorFlow

  •    Python

This is an example of using the new Google's TensorFlow library on monolingual translation going from modern English to Shakespeare based on research from Wei Xu. Delete /cache to start anew.

DCNMT - Deep Character-Level Neural Machine Translation

  •    Python

We implement a Deep Character-Level Neural Machine Translation based on Theano and Blocks. Please intall relative packages according to Blocks before testing our program. Note that, please use Python 3 instead of Python 2. There will be some problems with Python 2. The architecture of DCNMT is shown in the following figure which is a single, large neural network.

CTranslate - OpenNMT C++ translator

  •    C++

CTranslate is a C++ implementation of OpenNMT's translate.lua script with no LuaTorch dependencies. It facilitates the use of OpenNMT models in existing products and on various platforms using Eigen as a backend. CTranslate provides optimized CPU translation and optionally offloads matrix multiplication on a CUDA-compatible device using cuBLAS. It only supports OpenNMT models released with the release_model.lua script.

NPMT - Towards Neural Phrase-based Machine Translation

  •    Lua

This is NPMT, the source codes of Towards Neural Phrase-based Machine Translation and Sequence Modeling via Segmentations from Microsoft Research. It is built on top of the fairseq toolkit in Torch. We present the setup and Neural Machine Translation (NMT) experiments in Towards Neural Phrase-based Machine Translation. Neural Phrase-based Machine Translation (NPMT) explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new layer to perform (soft) local reordering of input sequences. Different from existing neural machine translation (NMT) approaches, NPMT does not use attention-based decoding mechanisms. Instead, it directly outputs phrases in a sequential order and can decode in linear time.

bytenet_translation - A TensorFlow Implementation of Machine Translation In Neural Machine Translation in Linear Time

  •    Python

After 15 epochs, I obtained the Bleu score 7.38, which is far from good. Maybe some part in the implementation is incorrect. Or maybe we need more data or a bigger model. Details are available in the results folder.