4 |
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Automatic classification of human translation and machine translation : a study from the perspective of lexical diversity
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Transformer-based NMT : modeling, training and implementation
|
|
Xu, Hongfei. - : Saarländische Universitäts- und Landesbibliothek, 2021
|
|
BASE
|
|
Show details
|
|
13 |
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
|
|
|
|
In: Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02892154 ; Language Resources and Evaluation Conference, ELDA/ELRA, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/en/ (2020)
|
|
BASE
|
|
Show details
|
|
14 |
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Linguistically inspired morphological inflection with a sequence to sequence model ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers ...
|
|
|
|
Abstract:
Due to its effectiveness and performance, the Transformer translation model has attracted wide attention, most recently in terms of probing-based approaches. Previous work focuses on using or probing source linguistic features in the encoder. To date, the way word translation evolves in Transformer layers has not yet been investigated. Naively, one might assume that encoder layers capture source information while decoder layers translate. In this work, we show that this is not quite the case: translation already happens progressively in encoder layers and even in the input embeddings. More surprisingly, we find that some of the lower decoder layers do not actually do that much decoding. We show all of this in terms of a probing approach where we project representations of the layer analyzed to the final trained and frozen classifier level of the Transformer decoder to measure word translation accuracy. Our findings motivate and explain a Transformer configuration change: if translation already happens in the ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://dx.doi.org/10.48550/arxiv.2003.09586 https://arxiv.org/abs/2003.09586
|
|
BASE
|
|
Hide details
|
|
19 |
Language service provision in the 21st century: challenges, opportunities and educational perspectives for translation studies
|
|
|
|
In: ISBN: 9788869234934 ; Bologna Process beyond 2020: Fundamental values of the EHEA pp. 297-303 (2020)
|
|
BASE
|
|
Show details
|
|
20 |
Deep interactive text prediction and quality estimation in translation interfaces
|
|
|
|
In: Hokamp, Christopher M. (2018) Deep interactive text prediction and quality estimation in translation interfaces. PhD thesis, Dublin City University. (2018)
|
|
BASE
|
|
Show details
|
|
|
|