5 citations · 11 across the 5 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
Neural Machine Translation Decoding with Terminology Constraints
Eva Hasler, Adrià De Gispert, Gonzalo Iglesias +1
Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology constra…
Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment
Gonzalo Iglesias, William Tambellini, Adrià De Gispert +2
We describe a batched beam decoding algorithm for NMT with LMBR n-gram posteriors, showing that LMBR techniques still yield gains on top of the best recently reported results with…
A Comparison of Neural Models for Word Ordering
Eva Hasler, Felix Stahlberg, Marcus Tomalin +2
We compare several language models for the word-ordering task and propose a new bag-to-sequence neural model based on attention-based sequence-to-sequence models. We evaluate the m…