Jason Smith

Richard Savery, Benjamin Genchel, Jason Smith et al.

Harriet Padberg wrote Computer-Composed Canon and Free Fugue as part of her 1964 dissertation in Mathematics and Music at Saint Louis University. This program is one of the earliest examples of text-to-music software and algorithmic composition, which are areas of great interest in the present-day field of music technology. This paper aims to analyze the technological innovation, aesthetic design process, and impact of Harriet Padberg's original 1964 thesis as well as the design of a modern recreation and utilization, in order to gain insight to the nature of revisiting older works. Here, we present our open source recreation of Padberg's program with a modern interface and, through its use as an artistic tool by three composers, show how historical works can be effectively used for new creative purposes in contemporary contexts. Not Even One by Molly Jones draws on the historical and social significance of Harriet Padberg through using her program in a piece about the lack of representation of women judges in composition competitions. Brevity by Anna Savery utilizes the original software design as a composition tool, and The Padberg Piano by Anthony Caulkins uses the melodic generation of the original to create a software instrument.

Yonghui Wu, Mike Schuster, Zhifeng Chen et al.

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. Also, most NMT systems have difficulty with rare words. These issues have hindered NMT's use in practical deployments and services, where both accuracy and speed are essential. In this work, we present GNMT, Google's Neural Machine Translation system, which attempts to address many of these issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using attention and residual connections. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve handling of rare words, we divide words into a limited set of common sub-word units ("wordpieces") for both input and output. This method provides a good balance between the flexibility of "character"-delimited models and the efficiency of "word"-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system. Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. On the WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google's phrase-based production system.