Compact Personalized Models for Neural Machine Translation

We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture - combining a state-of-the-art self-attentive model with compact domain adaptation - provides high quality personalized machine translation that is both space and time efficient.Comment: Published at the 2018 Conference on Empirical Methods in Natural Language Processin

Estimation of covariance components between one continuous and one binary trait

International audienc

Optimum allocation of conservation funds and choice of conservation programs for a set of African cattle breeds

<p>Abstract</p> <p>Although funds for livestock conservation are limited there is little known about the optimal allocation of conservation funds. A new algorithm was used to allocate Mio US$1, 2, 3, 5 or unlimited funds, discounted over 50 years, on 23 African cattle breeds conserved with four different possible conservation programs. Additionally, Mio US$ 1 was preferably allocated to breeds with special traits. The conceptional <it>in situ </it>conservation programs strongly involve breeders and give them part of the responsibility for the conservation of the breed. Therefore, the pure <it>in situ </it>conservation was more efficient than cryoconservation or combined <it>in situ </it>and cryoconservation. The average annual discounted conservation cost for a breed can be as low as US$1000 to US$ 4400 depending on the design of the conservation program and the economic situation of the country of conservation. The choice of the breeds and the optimal conservation program and the amount of money allocated to each breed depend on many factors such as the amount of funds available, the conservation potential of each breed, the effects of the conservation program as well as its cost. With Mio US$1, 64$ 10, leaving 8% of the diversity to unpredictable happenings. The suggested algorithm proved to be useful for optimal allocation of conservation funds. It allocated the funds optimally among breeds by identifying the most suited conservation program for each breed, also accounting for differences in currency exchange rates between the different countries.</p

Comparison of traditional and genomic breeding programs for organic and low input dairy cattle accounting for traits relevant in different macro-climatic zones

In the past decade, successful selection on production traits for dairy cattle has greatly increased milk production. Recently, selection indices for female fertility were gradually and increasingly introduced into the overall breeding goals for dairy cattle (Miglior et al., 2005). As a by-product of fermention in ruminants, enteric methane emissions (ME) should also be controlled and mitigated due to their contribution to global warming (Forster et al., 2007) and as a cause for inefficient use of dietary energy. Moderate heritabilities ranging between 0.30 and 0.35 for predicted and real measurements of ME were reported for dairy cows and ewe lambs (de Haas et al., 2011; Pinares-Patiño et al., 2011), indicating that a heritable component for ME is available for implementing sustainable breeding strategies to reduce ME in dairy farms. In dairy cattle production systems, the traditional progeny testing substantially increases accuracy of selection especially for bulls. However, availability of high-density SNP arrays enable dairy cattle breeders to apply genomic selection in their breeding strategies. Consequently, the objective of this study was to compare selection response for a complex breeding goal comprising ME, milk yield (MY), days open (DO), clinical mastitis (CM), body condition score (BCS) and milking temperament (MT) and total discounted return for organic and low input dairy cattle (with organic Brown Swiss as an example) from progeny testing and genomic breeding program by applying ZPLAN+ (Täubert et al., 2010)

Optimum allocation of conservation funds and choice of conservation programs for a set of African cattle breeds

Although funds for livestock conservation are limited there is little known about the optimal allocation of conservation funds. A new algorithm was used to allocate Mio US$1, 2, 3, 5 or unlimited funds, discounted over 50 years, on 23 African cattle breeds conserved with four different possible conservation programs. Additionally, Mio US$ 1 was preferably allocated to breeds with special traits. The conceptional in situ conservation programs strongly involve breeders and give them part of the responsibility for the conservation of the breed. Therefore, the pure in situ conservation was more efficient than cryoconservation or combined in situ and cryoconservation. The average annual discounted conservation cost for a breed can be as low as US$1000 to US$ 4400 depending on the design of the conservation program and the economic situation of the country of conservation. The choice of the breeds and the optimal conservation program and the amount of money allocated to each breed depend on many factors such as the amount of funds available, the conservation potential of each breed, the effects of the conservation program as well as its cost. With Mio US$1, 64$ 10, leaving 8% of the diversity to unpredictable happenings. The suggested algorithm proved to be useful for optimal allocation of conservation funds. It allocated the funds optimally among breeds by identifying the most suited conservation program for each breed, also accounting for differences in currency exchange rates between the different countries

Preference Learning for Machine Translation

Automatic translation of natural language is still (as of 2017) a long-standing but unmet promise. While advancing at a fast rate, the underlying methods are still far from actually being able to reliably capture syntax or semantics of arbitrary utterances of natural language, way off transporting the encoded meaning into a second language. However, it is possible to build useful translating machines when the target domain is well known and the machine is able to learn and adapt efficiently and promptly from new inputs. This is possible thanks to efficient and effective machine learning methods which can be applied to automatic translation. In this work we present and evaluate methods for three distinct scenarios: a) We develop algorithms that can learn from very large amounts of data by exploiting pairwise preferences defined over competing translations, which can be used to make a machine translation system robust to arbitrary texts from varied sources, but also enable it to learn effectively to adapt to new domains of data; b) We describe a method that is able to efficiently learn external models which adhere to fine-grained preferences that are extracted from a constricted selection of translated material, e.g. for adapting to users or groups of users in a computer-aided translation scenario; c) We develop methods for two machine translation paradigms, neural- and traditional statistical machine translation, to directly adapt to user-defined preferences in an interactive post-editing scenario, learning precisely adapted machine translation systems. In all of these settings, we show that machine translation can be made significantly more useful by careful optimization via preference learning

Genetische Parameter für verschiedene euterviertelspezifische Merkmale beim Schweizer Braunvieh

Fragestellung: - Gibt es Unterschiede und Regelmäßigkeiten in den genetischen Parametern für die Milchinhaltsstoffe zwischen den Eutervierteln? - Lassen sich diese Informationen züchterisch nutzen