Between words and characters: A brief history of open-vocabulary modeling and tokenization in NLP
What are the units of text that we want to model? From bytes to multi-word expressions, text
can be analyzed and generated at many granularities. Until recently, most natural language …
can be analyzed and generated at many granularities. Until recently, most natural language …
Why don't people use character-level machine translation?
We present a literature and empirical survey that critically assesses the state of the art in
character-level modeling for machine translation (MT). Despite evidence in the literature that …
character-level modeling for machine translation (MT). Despite evidence in the literature that …
[HTML][HTML] A reverse positional encoding multi-head attention-based neural machine translation model for arabic dialects
Languages with a grammatical structure that have a free order for words, such as Arabic
dialects, are considered a challenge for neural machine translation (NMT) models because …
dialects, are considered a challenge for neural machine translation (NMT) models because …
On sparsifying encoder outputs in sequence-to-sequence models
Sequence-to-sequence models usually transfer all encoder outputs to the decoder for
generation. In this work, by contrast, we hypothesize that these encoder outputs can be …
generation. In this work, by contrast, we hypothesize that these encoder outputs can be …
When is char better than subword: A systematic study of segmentation algorithms for neural machine translation
Subword segmentation algorithms have been a de facto choice when building neural
machine translation systems. However, most of them need to learn a segmentation model …
machine translation systems. However, most of them need to learn a segmentation model …
Local byte fusion for neural machine translation
Subword tokenization schemes are the dominant technique used in current NLP models.
However, such schemes can be rigid and tokenizers built on one corpus do not adapt well to …
However, such schemes can be rigid and tokenizers built on one corpus do not adapt well to …
Evaluating morphological generalisation in machine translation by distribution-based compositionality assessment
Compositional generalisation refers to the ability to understand and generate a potentially
infinite number of novel meanings using a finite group of known primitives and a set of rules …
infinite number of novel meanings using a finite group of known primitives and a set of rules …
The boundaries of meaning: a case study in neural machine translation
Y Balashov - Inquiry, 2022 - Taylor & Francis
The success of deep learning in natural language processing raises intriguing questions
about the nature of linguistic meaning and ways in which it can be processed by natural and …
about the nature of linguistic meaning and ways in which it can be processed by natural and …
The LMU Munich systems for the WMT21 unsupervised and very low-resource translation task
J Libovický, A Fraser - Proceedings of the Sixth Conference on …, 2021 - aclanthology.org
We present our submissions to the WMT21 shared task in Unsupervised and Very Low
Resource machine translation between German and Upper Sorbian, German and Lower …
Resource machine translation between German and Upper Sorbian, German and Lower …
Towards efficient universal neural machine translation
B Zhang - 2022 - era.ed.ac.uk
Humans benefit from communication but suffer from language barriers. Machine translation
(MT) aims to overcome such barriers by automatically transforming information from one …
(MT) aims to overcome such barriers by automatically transforming information from one …