A user-study on online adaptation of neural machine translation to human post-edits

© 2018, Springer Nature B.V. The advantages of neural machine translation (NMT) have been extensively validated for offline translation of several language pairs for different domains of spoken and written language. However, research on interactive learning of NMT by adaptation to human post-edits has so far been confined to simulation experiments. We present the first user study on online adaptation of NMT to user post-edits in the domain of patent translation. Our study involves 29 human subjects (translation students) whose post-editing effort and translation quality were measured on about 4500 interactions of a human post-editor and an NMT system integrating an online adaptive learning algorithm. Our experimental results show a significant reduction in human post-editing effort due to online adaptation in NMT according to several evaluation metrics, including hTER, hBLEU, and KSMR. Furthermore, we found significant improvements in BLEU/TER between NMT outputs and professional translations in granted patents, providing further evidence for the advantages of online adaptive NMT in an interactive setup

Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search

Retrieval pipelines commonly rely on a term-based search to obtain candidate records, which are subsequently re-ranked. Some candidates are missed by this approach, e.g., due to a vocabulary mismatch. We address this issue by replacing the term-based search with a generic k-NN retrieval algorithm, where a similarity function can take into account subtle term associations. While an exact brute-force k-NN search using this similarity function is slow, we demonstrate that an approximate algorithm can be nearly two orders of magnitude faster at the expense of only a small loss in accuracy. A retrieval pipeline using an approximate k-NN search can be more effective and efficient than the term-based pipeline. This opens up new possibilities for designing effective retrieval pipelines. Our software (including data-generating code) and derivative data based on the Stack Overflow collection is available online

A user-study on online adaptation of neural machine translation to human post-edits

© 2018, Springer Nature B.V. The advantages of neural machine translation (NMT) have been extensively validated for offline translation of several language pairs for different domains of spoken and written language. However, research on interactive learning of NMT by adaptation to human post-edits has so far been confined to simulation experiments. We present the first user study on online adaptation of NMT to user post-edits in the domain of patent translation. Our study involves 29 human subjects (translation students) whose post-editing effort and translation quality were measured on about 4500 interactions of a human post-editor and an NMT system integrating an online adaptive learning algorithm. Our experimental results show a significant reduction in human post-editing effort due to online adaptation in NMT according to several evaluation metrics, including hTER, hBLEU, and KSMR. Furthermore, we found significant improvements in BLEU/TER between NMT outputs and professional translations in granted patents, providing further evidence for the advantages of online adaptive NMT in an interactive setup

Learning Dense Models of Query Similarity from User Click Logs

The goal of this work is to integrate query similarity metrics as features into a dense model that can be trained on large amounts of query log data, in order to rank query rewrites. We propose features that incorporate various notions of syntactic and semantic similarity in a generalized edit distance frame-work. We use the implicit feedback of user clicks on search results as weak labels in training linear ranking models on large data sets. We optimize different ranking objectives in a stochastic gradient descent framework. Our experiments show that a pairwise SVM ranker trained on multipartite rank levels outperforms other pairwise and listwise ranking methods under a variety of evaluation metrics

Vergleich von Praxiskonzepten zur wertorientierten Unternehmenssteuerung

Der Arbeitskreis Internes Rechnungswesen gibt einen Einblick in die wertorientierten Steuerungskonzepte der Unternehmen BASF, RWE, ThyssenKrupp, Volkswagen und BOSCH. Dabei wird deutlich, dass trotz aller Plausibilität und Einfachheit der Grundidee ihre Umsetzung in die Praxis schwierig ist und angepasst an die konkreten Rahmenbedingungen eines Unternehmens wie Größe, Risikoprofil und Ziele, aber auch in Abhängigkeit des historisch gewachsenen Steuerungsverständnisses eines Konzerns erfolgt. Das Bild der wertorientierten Steuerung fällt dementsprechend differenziert aus. Auch werden einige technische Umsetzungsfragen nach wie vor kontrovers diskutiert. The German working group Internes Rechnungswesen provides insight into value-based management concepts based on experience with BASF, RWE, ThyssenKrupp, Volkswagen and Bosch. It is shown that, in spite of the plausibility and simplicity of the basic idea underlying value-based management, the practical implementation remains challenging and always requires adjustments to suit the characteristics of corporations such as their size, risk profile or strategic goals as well as their management philosophy. Therefore, the findings regarding the utilization of value-based management vary considerably. Furthermore, some technical questions related to implementation, which are still controversial, are discussed