The RWTH system for the IWSLT 2007 evaluation is a combination of several statistical machine translation systems. The combination includes Phrase-Based models, a n-gram translation model and a hierarchical phrase mod...
详细信息
Back-translation - data augmentation by translating target monolingual data - is a crucial component in modern neural machine translation (NMT). In this work, we reformulate back-translation in the scope of crossentro...
详细信息
Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the c...
详细信息
We present a Minimum Bayes Risk (MBR) decoder for statistical machine translation. The approach aims to minimize the expected loss of translation errors with regard to the BLEU score. We show that MBR decoding on N-be...
详细信息
We give an overview of the RWTH phrase-based statistical machine translation system that was used in the evaluation campaign of the International Workshop on Spoken language Translation (IWSLT) 2006. The system was ra...
详细信息
This paper studies the practicality of the current state-of-the-art unsupervised methods in neural machine translation (NMT). In ten translation tasks with various data settings, we analyze the conditions under which ...
详细信息
We present a method to fully automatically fit videos in 16:9 format on 4:3 screens and vice versa. It can be applied to arbitrary aspect ratios and can be used to make videos suitable for mobile viewing devices with ...
详细信息
We present a method to fully automatically fit videos in 16:9 format on 4:3 screens and vice versa. It can be applied to arbitrary aspect ratios and can be used to make videos suitable for mobile viewing devices with small and possibly uncommonly sized displays. The cropping sequence is optimised over time to create smooth transitions and thus leads to an excellent viewing experience. Current televisions have simple and often disturbing methods which either show the centre region of the image, distort the image, or pad it with black borders. The technique presented here can fully automatically find the "right" viewing area for each image in a video sequence. It works in real-time with only very little time-shift. We employ different low-level features and a log-linear model to learn how to find the right area. The method is able to automatically decide whether padding with black borders is necessary or whether all relevant image areas fit on screen by cropping the image. Evaluation is done on ten videos from five different types of content and the baseline methods are clearly outperformed.
Word posterior probabilities are a common approach for confidence estimation in automatic speech recognition and machine translation. We will generalize this idea and introduce n-gram posterior probabilities and show ...
详细信息
We propose and study three different novel approaches for tackling the problem of development set selection in Statistical Machine Translation. We focus on a scenario where a machine translation system is leveraged fo...
详细信息
We show that most search errors can be identified by aligning the results of a symmetric forward and backward decoding pass. Based on this knowledge, we introduce an efficient high-level decoding architecture which yi...
详细信息
ISBN:
(纸本)9781479927579
We show that most search errors can be identified by aligning the results of a symmetric forward and backward decoding pass. Based on this knowledge, we introduce an efficient high-level decoding architecture which yields virtually no search errors, and requires virtually no manual tuning. We perform an initial forward- and backward decoding with tight initial beams, then we identify search errors, and then we recursively increment the beam sizes and perform new forward and backward decodings for erroneous intervals until no more search errors are detected. Consequently, each utterance and even each single word is decoded with the smallest beam size required to decode it correctly. On all tested systems we achieve an error rate equal or very close to classical decoding with ideally tuned beam size, but unsupervisedly without specific tuning, and at around 2 times faster runtime. An additional speedup by factor 2 can be achieved by decoding the forward and backward pass in separate threads.
暂无评论