DE eng

Search in the Catalogues and Directories

Hits 1 – 12 of 12

1
Europarl Direct Translationese Dataset ...
BASE
Show details
2
Europarl Direct Translationese Dataset ...
BASE
Show details
3
Europarl Direct Translationese Dataset ...
BASE
Show details
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
Fu, Yingxue; Nederhof, Mark Jan. - : Linkoping University Electronic Press, 2021
Abstract: By using a trigram model and fine-tuning a pretrained BERT model for sequence classification, we show that machine translation and human translation can be classified with an accuracy above chance level, which suggests that machine translation and human translation are different in a systematic way. The classification accuracy of machine translation is much higher than of human translation. We show that this may be explained by the difference in lexical diversity between machine translation and human translation. If machine translation has independent patterns from human translation, automatic metrics which measure the deviation of machine translation from human translation may conflate difference with quality. Our experiment with two different types of automatic metrics shows correlation with the result of the classification task. Therefore, we suggest the difference in lexical diversity between machine translation and human translation be given more attention in machine translation evaluation. ; Publisher PDF
Keyword: 3rd-DAS; Artificial Intelligence; Q Science (General); Q1
URL: https://aclanthology.org/previews/ingest-nodalida/2021.motra-1.10/
http://hdl.handle.net/10023/23304
BASE
Hide details
12
Transformer-based NMT : modeling, training and implementation
Xu, Hongfei. - : Saarländische Universitäts- und Landesbibliothek, 2021
BASE
Show details

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
12
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern