282 citations · 1.2k across the 80 of their papers we have counts for
6 papers · 2 filters
Improving Long Context Document-Level Machine Translation
Christian Herold, Hermann Ney
Document-level context for neural machine translation (NMT) is crucial to improve the translation consistency and cohesion, the translation of ambiguous inputs, as well as several…
On Search Strategies for Document-Level Neural Machine Translation
Christian Herold, Hermann Ney
Compared to sentence-level systems, document-level neural machine translation (NMT) models produce a more consistent output across a document and are able to better resolve ambigui…
Improving Language Model Integration for Neural Machine Translation
Christian Herold, Yingbo Gao, Mohammad Zeineldeen +1
The integration of language models for neural machine translation has been extensively studied in the past. It has been shown that an external language model, trained on additional…
RASR2: The RWTH ASR Toolkit for Generic Sequence-to-sequence Speech Recognition
Wei Zhou, Eugen Beck, Simon Berger +2
Modern public ASR tools usually provide rich support for training various sequence-to-sequence (S2S) models, but rather simple support for decoding open-vocabulary scenarios only.…
Task-oriented Document-Grounded Dialog Systems by HLTPR@RWTH for DSTC9 and DSTC10
David Thulke, Nico Daheim, Christian Dugast +1
This paper summarizes our contributions to the document-grounded dialog tasks at the 9th and 10th Dialog System Technology Challenges (DSTC9 and DSTC10). In both iterations the tas…
Analyzing And Improving Neural Speaker Embeddings for ASR
Christoph Lüscher, Jingjing Xu, Mohammad Zeineldeen +2
Neural speaker embeddings encode the speaker's speech characteristics through a DNN model and are prevalent for speaker verification tasks. However, few studies have investigated t…