State-of-the-art neural machine translation (NMT) models deliver high-quality translations at the expense of high inference latency and energy consumption, requiring vast GPU fleets and contributing significantly to carbon emissions. To democratize and “green” NMT, we introduce the Green KNIGHT, a hardware-agnostic collection of recipes to optimize translation speed and energy consumption, with only a moderate trade-off in quality. On high-resource En→De and En→Ko benchmarks, we achieve up to 117× CPU speedup and 98.2% energy savings with 9% relative BLEU decrease. On WMT 2014 En→De and En→Fr benchmarks, we obtain up to 140× speedup with 98.7% energy savings, while staying within 10–12% relative BLEU decrease. Our results demonstrate that efficient and environmentally conscious NMT can be realized through optimizations built on well-understood, off-the-shelf techniques with no custom low-level code required, making our approach immediately deployable in real-world translation pipelines.

AppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020. For the offline task, we create both cascaded and end-to-end speech translation systems, paying attention to careful data selection and weighting. In the cascaded approach, we combine high-quality hybrid automatic speech recognition (ASR) with the Transformer-based neural machine translation (NMT). Our end-to-end direct speech translation systems benefit from pretraining of adapted encoder and decoder components, as well as synthetic data and fine-tuning and thus are able to compete with cascaded systems in terms of MT quality. For simultaneous translation, we utilize a novel architecture that makes dynamic decisions, learned from parallel data, to determine when to continue feeding on input or generate output words. Experiments with speech and text input show that even at low latency this architecture leads to superior translation results.

This work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of International Workshop on Spoken Language Translation (IWSLT) 2016. We have participated in the MT track for the German→English language pair employing our state-of-the-art phrase-based system, neural machine translation implementation and our joint translation and reordering decoder. Furthermore, we have applied feed-forward and recurrent neural language and translation models for reranking. The attention-based approach has been used for reranking the n-best lists for both phrasebased and hierarchical setups. On top of these systems, we make use of system combination to enhance the translation quality by combining individually trained systems.

This work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign International Workshop on Spoken Language Translation (IWSLT) 2014. We participated in both the MT and SLT tracks for the English→French and German→English language pairs and applied the identical training pipeline and models on both language pairs. Our state-of-the-art phrase-based baseline systems are augmented with maximum expected BLEU training for phrasal, lexical and reordering models. Further, we apply rescoring with novel recurrent neural language and translation models. The same systems are used for the SLT track, where we additionally perform punctuation prediction on the automatic transcriptions employing hierarchical phrase-based translation. We are able to improve RWTH’s 2013 evaluation systems by 1.7-1.8% BLEU absolute.