Checkpoint averaging is a simple and effectivemethod to boost the performance of convergedneural machine translation models. The calculation is cheap to perform and the fact thatthe translation improvement almost come...
详细信息
In this paper, we enhance the traditional confusion network system combination approach with an additional model trained by a neural network. This work is motivated by the fact that the commonly used binary system vot...
详细信息
This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the German!English translation task of the ACL 2014 Eighth Workshop on Statistical Machine Translation (WM...
详细信息
This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English ↔ German news translation task of the EMNLP 2018 Third Conference on Machine Trans...
详细信息
Embedding and projection matrices are commonly used in neural language models (NLM) as well as in other sequence processing networks that operate on large vocabularies. We examine such matrices in fine-tuned language ...
详细信息
This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the translation task of the ACL 2013 Eighth Workshop on Statistical Machine Translation (WMT 2013). We par...
详细信息
This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the translation task of the NAACL 2012 Seventh Workshop on Statistical Machine Translation (WMT 2012). We ...
详细信息
We investigate insertion and deletion models for hierarchical phrase-based statistical machine translation. Insertion and deletion models are designed as a means to avoid the omission of content words in the hypothese...
详细信息
This paper describes the statistical machine translation system developed at RWTH Aachen University for the English?German and German?English translation tasks of the EMNLP 2017 Second Conference on Machine Translatio...
详细信息
This work investigates the alignment problem in state-of-the-art multi-head attention models based on the transformer architecture. We demonstrate that alignment extraction in transformer models can be improved by aug...
详细信息
暂无评论