Neural Monkey: An Open-source Tool for Sequence Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10372014" target="_blank" >RIV/00216208:11320/17:10372014 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1515/pralin-2017-0001" target="_blank" >http://dx.doi.org/10.1515/pralin-2017-0001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1515/pralin-2017-0001" target="_blank" >10.1515/pralin-2017-0001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural Monkey: An Open-source Tool for Sequence Learning

Popis výsledku v původním jazyce

In this paper, we announce development of Neural Monkey - an open-source neural machine translation (NMT) and general sequence-to-sequence learning system built over TensorFlow machine learning library. The system provides a high-level API with support for fast prototyping of complex architectures with multiple sequence encoders and decoders. These models&apos; overall architecture is specified in easy-to-read configuration files. The long-term goal of Neural Monkey project is to create and maintain a growing collection of implementations of recently proposed components or methods, and therefore it is designed to be easily extensible. The trained models can be deployed either for batch data processing or as a web service. In the presented paper, we describe the design of the system and introduce the reader to running experiments using Neural Monkey.

Název v anglickém jazyce

Neural Monkey: An Open-source Tool for Sequence Learning

Popis výsledku anglicky

In this paper, we announce development of Neural Monkey - an open-source neural machine translation (NMT) and general sequence-to-sequence learning system built over TensorFlow machine learning library. The system provides a high-level API with support for fast prototyping of complex architectures with multiple sequence encoders and decoders. These models&apos; overall architecture is specified in easy-to-read configuration files. The long-term goal of Neural Monkey project is to create and maintain a growing collection of implementations of recently proposed components or methods, and therefore it is designed to be easily extensible. The trained models can be deployed either for batch data processing or as a web service. In the presented paper, we describe the design of the system and introduce the reader to running experiments using Neural Monkey.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

The Prague Bulletin of Mathematical Linguistics

ISSN

0032-6585

e-ISSN

—

Svazek periodika

Neuveden

Číslo periodika v rámci svazku

107

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

13

Strana od-do

5-17

Kód UT WoS článku

—

EID výsledku v databázi Scopus

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