5 citations · 11 across the 5 of their papers we have counts for
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cs.CL2022★ 5 cited
Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translation
Tsz Kin Lam, Eva Hasler, Felix Hieber
Customer feedback can be an important signal for improving commercial machine translation systems. One solution for fixing specific translation errors is to remove the related erro…
cs.CL2022★ 5 cited
Automatic Evaluation and Analysis of Idioms in Neural Machine Translation
Christos Baziotis, Prashant Mathur, Eva Hasler
A major open problem in neural machine translation (NMT) is the translation of idiomatic expressions, such as "under the weather". The meaning of these expressions is not composed…
cs.CL2022
The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation
Tobias Domhan, Eva Hasler, Ke Tran +3
Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words dur…