Resistance and accommodation: factors for the (non-) adoption of machine translation among professional translators

By Patrick Cadwell, Sharon O’Brien, Carlos S. C. Teixeira. 2017

118 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Language and Linguistics

View Paper: https://www.semanticscholar.org/paper/dafd7a6c138159ce263b355a18c1665a34b8f09a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Philipp Koehn
2. Kevin Duh
3. Huda Khayrallah
4. Brian Thompson
5. Felix Stahlberg
6. Jeremy Gwinnup
7. Arda Tezcan
8. Bram Bulté

In Information Systems

1. Brian Thompson
2. Philipp Koehn
3. Kevin Duh
4. Bram Bulté
5. Arda Tezcan

In Language and Linguistics

1. Brian Thompson
2. Philipp Koehn
3. Huda Khayrallah
4. Bram Bulté
5. Arda Tezcan
6. Scott Andrew Crossley
7. N. Vyatkina

Citations in the corpus

• Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation (2019, methodology): “Moreover, it is possible that NFR systems help to lower the resistance some translators have to adopting MT (Cadwell et al., 2018),”
• Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation (2019, background): “See Cadwell et al. (2018) and Porro Rodriguez et al.”