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Through the development of neural machine translation, the quality of machine translation systems has been improved significantly. By exploiting advancements in deep learning, systems are now able to better approximate the complex mapping from source sentences to target sentences. But with this ability, new challenges also arise. An example is the translation of partial sentences in low-latency speech translation. Since the model has only seen complete sentences in training, it will always try to generate a complete sentence, though the input may only be a partial sentence. We show that NMT systems can be adapted to scenarios where no task-specific training data is available. Furthermore, this is possible without losing performance on the original training data. We achieve this by creating artificial data and by using multi-task learning. After adaptation, we are able to reduce the number of corrections displayed during incremental output construction by 45%, without a decrease in translation quality.

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An important concern in training multilingual neural machine translation (NMT) is to translate between language pairs unseen during training, i.e zero-shot translation. Improving this ability kills two birds with one stone by providing an alternative to pivot translation which also allows us to better understand how the model captures information between languages.In this work, we carried out an investigation on this capability of the multilingual NMT models. First, we intentionally create an encoder architecture which is independent with respect to the source language. Such experiments shed light on the ability of NMT encoders to learn multilingual representations, in general. Based on such proof of concept, we were able to design regularization methods into the standard Transformer model, so that the whole architecture becomes more robust in zero-shot conditions. We investigated the behaviour of such models on the standard IWSLT 2017 multilingual dataset. We achieved an average improvement of 2.23 BLEU points across 12 language pairs compared to the zero-shot performance of a state-of-the-art multilingual system. Additionally, we carry out further experiments in which the effect is confirmed even for language pairs with multiple intermediate pivots.

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This paper describes KIT's submissions to the IWSLT2020 Speech Translation evaluation campaign. We first participate in the simultaneous translation task, in which our simultaneous models are Transformer-based and can be efficiently trained to obtain low latency with minimized compromise in quality. On the offline speech translation task, we applied our new Speech Transformer architecture to endto-end speech translation. The obtained model can provide translation quality which is competitive to a complicated cascade. The latter still has the upper hand, thanks to the ability to transparently access to the transcription, and resegment the inputs to avoid fragmentation.

Foreign students at German universities often have difficulties following lectures as they are often held in German. Since human interpreters are too expensive for universities we are addressing this problem via speech translation technology deployed in KIT's lecture halls. Our simultaneous lecture translation system automatically translates lectures from German to English in real-time. Other supported language directions are English to Spanish, English to French, English to German and German to French. Automatic simultaneous translation is more than just the concatenation of automatic speech recognition and machine translation technology, as the input is an unsegmented, practically infinite stream of spontaneous speech. The lack of segmentation and the spontaneous nature of the speech makes it especially difficult to recognize and translate it with sufficient quality. In addition to quality, speed and latency are of the utmost importance in order for the system to enable students to follow lectures. In this paper we present our system that performs the task of simultaneous speech translation of university lectures by performing speech translation on a stream of audio in real-time and with low latency. The system features several techniques beyond the basic speech translation task, that make it fit for real-world use. Examples of these features are a continuous stream speech recognition without any prior segmentation of the input audio, punctuation prediction, run-on decoding and run-on translation with continuously updating displays in order to keep the latency as low as possible.

Neural network language and translation models have recently shown their great potentials in improving the performance of phrase-based machine translation. At the same time, word representations using different word factors have been translation quality and are part of many state-of-theart machine translation systems. used in many state-of-the-art machine translation systems, in order to support better translation quality. In this work, we combined these two ideas by investigating the combination of both techniques. By representing words in neural network language models using different factors, we were able to improve the models themselves as well as their impact on the overall machine translation performance. This is especially helpful for morphologically rich languages due to their large vocabulary size. Furthermore, it is easy to add additional knowledge, such as source side information, to the model. Using this model we improved the translation quality of a state-of-the-art phrasebased machine translation system by 0.7 BLEU points. We performed experiments on three language pairs for the news translation task of the WMT 2016 evaluation.