January 1, 2017Open Access

What do Neural Machine Translation Models Learn about Morphology?

Puntos clave

Los puntos clave no están disponibles para este artículo en este momento.

Resumen

Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the training process.

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo

Cite This Study

Belinkov et al. (Sun,) studied this question.

synapsesocial.com/papers/6a0dadeececdf5fb20ba8d86 https://doi.org/https://doi.org/10.18653/v1/p17-1080

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Combining Word-Level and Character-Level Models for Machine Translation Between Closely-Related Languages2012 · 87 citations
2From phonemes to images: levels of representation in a recurrent neural\n model of visually-grounded language learning2016 · 11 citations
3Factored Translation Models2007 · 510 citations
4Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks2024 · 243 citations
5Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging2024 · 13 citations

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo