Gregor Leusch


2018

pdf bib
Quality Estimation for Automatically Generated Titles of eCommerce Browse Pages
Nicola Ueffing | José G. C. de Souza | Gregor Leusch
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

At eBay, we are automatically generating a large amount of natural language titles for eCommerce browse pages using machine translation (MT) technology. While automatic approaches can generate millions of titles very fast, they are prone to errors. We therefore develop quality estimation (QE) methods which can automatically detect titles with low quality in order to prevent them from going live. In this paper, we present different approaches: The first one is a Random Forest (RF) model that explores hand-crafted, robust features, which are a mix of established features commonly used in Machine Translation Quality Estimation (MTQE) and new features developed specifically for our task. The second model is based on Siamese Networks (SNs) which embed the metadata input sequence and the generated title in the same space and do not require hand-crafted features at all. We thoroughly evaluate and compare those approaches on in-house data. While the RF models are competitive for scenarios with smaller amounts of training data and somewhat more robust, they are clearly outperformed by the SN models when the amount of training data is larger.

pdf bib
Multi-lingual neural title generation for e-Commerce browse pages
Prashant Mathur | Nicola Ueffing | Gregor Leusch
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

To provide better access of the inventory to buyers and better search engine optimization, e-Commerce websites are automatically generating millions of browse pages. A browse page consists of a set of slot name/value pairs within a given category, grouping multiple items which share some characteristics. These browse pages require a title describing the content of the page. Since the number of browse pages are huge, manual creation of these titles is infeasible. Previous statistical and neural approaches depend heavily on the availability of large amounts of data in a language. In this research, we apply sequence-to-sequence models to generate titles for high-resource as well as low-resource languages by leveraging transfer learning. We train these models on multi-lingual data, thereby creating one joint model which can generate titles in various different languages. Performance of the title generation system is evaluated on three different languages; English, German, and French, with a particular focus on low-resourced French language.

2017

pdf bib
Generating titles for millions of browse pages on an e-Commerce site
Prashant Mathur | Nicola Ueffing | Gregor Leusch
Proceedings of the 10th International Conference on Natural Language Generation

We present two approaches to generate titles for browse pages in five different languages, namely English, German, French, Italian and Spanish. These browse pages are structured search pages in an e-commerce domain. We first present a rule-based approach to generate these browse page titles. In addition, we also present a hybrid approach which uses a phrase-based statistical machine translation engine on top of the rule-based system to assemble the best title. For the two languages English and German we have access to a large amount of already available rule-based generated and curated titles. For these languages we present an automatic post-editing approach which learns how to post-edit the rule-based titles into curated titles.

2013

pdf bib
Omnifluent English-to-French and Russian-to-English Systems for the 2013 Workshop on Statistical Machine Translation
Evgeny Matusov | Gregor Leusch
Proceedings of the Eighth Workshop on Statistical Machine Translation

pdf bib
Selective Combination of Pivot and Direct Statistical Machine Translation Models
Ahmed El Kholy | Nizar Habash | Gregor Leusch | Evgeny Matusov | Hassan Sawaf
Proceedings of the Sixth International Joint Conference on Natural Language Processing

pdf bib
Language Independent Connectivity Strength Features for Phrase Pivot Statistical Machine Translation
Ahmed El Kholy | Nizar Habash | Gregor Leusch | Evgeny Matusov | Hassan Sawaf
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

pdf bib
Review of Hypothesis Alignment Algorithms for MT System Combination via Confusion Network Decoding
Antti-Veikko Rosti | Xiaodong He | Damianos Karakos | Gregor Leusch | Yuan Cao | Markus Freitag | Spyros Matsoukas | Hermann Ney | Jason Smith | Bing Zhang
Proceedings of the Seventh Workshop on Statistical Machine Translation

2011

pdf bib
The RWTH System Combination System for WMT 2011
Gregor Leusch | Markus Freitag | Hermann Ney
Proceedings of the Sixth Workshop on Statistical Machine Translation

pdf bib
Joint WMT Submission of the QUAERO Project
Markus Freitag | Gregor Leusch | Joern Wuebker | Stephan Peitz | Hermann Ney | Teresa Herrmann | Jan Niehues | Alex Waibel | Alexandre Allauzen | Gilles Adda | Josep Maria Crego | Bianka Buschbeck | Tonio Wandmacher | Jean Senellart
Proceedings of the Sixth Workshop on Statistical Machine Translation

pdf bib
The RWTH Aachen Machine Translation System for WMT 2011
Matthias Huck | Joern Wuebker | Christoph Schmidt | Markus Freitag | Stephan Peitz | Daniel Stein | Arnaud Dagnelies | Saab Mansour | Gregor Leusch | Hermann Ney
Proceedings of the Sixth Workshop on Statistical Machine Translation

2010

pdf bib
Multi-pivot translation by system combination
Gregor Leusch | Aurélien Max | Josep Maria Crego | Hermann Ney
Proceedings of the 7th International Workshop on Spoken Language Translation: Papers

This paper describes a technique to exploit multiple pivot languages when using machine translation (MT) on language pairs with scarce bilingual resources, or where no translation system for a language pair is available. The principal idea is to generate intermediate translations in several pivot languages, translate them separately into the target language, and generate a consensus translation out of these using MT system combination techniques. Our technique can also be applied when a translation system for a language pair is available, but is limited in its translation accuracy because of scarce resources. Using statistical MT systems for the 11 different languages of Europarl, we show experimentally that a direct translation system can be replaced by this pivot approach without a loss in translation quality if about six pivot languages are available. Furthermore, we can already improve an existing MT system by adding two pivot systems to it. The maximum improvement was found to be 1.4% abs. in BLEU in our experiments for 8 or more pivot languages.

pdf bib
The RWTH Aachen Machine Translation System for WMT 2010
Carmen Heger | Joern Wuebker | Matthias Huck | Gregor Leusch | Saab Mansour | Daniel Stein | Hermann Ney
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

pdf bib
The RWTH System Combination System for WMT 2010
Gregor Leusch | Hermann Ney
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

2009

pdf bib
The RWTH System Combination System for WMT 2009
Gregor Leusch | Evgeny Matusov | Hermann Ney
Proceedings of the Fourth Workshop on Statistical Machine Translation

2008

pdf bib
Complexity of Finding the BLEU-optimal Hypothesis in a Confusion Network
Gregor Leusch | Evgeny Matusov | Hermann Ney
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

pdf bib
Human Evaluation of Machine Translation Through Binary System Comparisons
David Vilar | Gregor Leusch | Hermann Ney | Rafael E. Banchs
Proceedings of the Second Workshop on Statistical Machine Translation

pdf bib
The RWTH machine translation system for IWSLT 2007
Arne Mauser | David Vilar | Gregor Leusch | Yuqi Zhang | Hermann Ney
Proceedings of the Fourth International Workshop on Spoken Language Translation

The RWTH system for the IWSLT 2007 evaluation is a combination of several statistical machine translation systems. The combination includes Phrase-Based models, a n-gram translation model and a hierarchical phrase model. We describe the individual systems and the method that was used for combining the system outputs. Compared to our 2006 system, we newly introduce a hierarchical phrase-based translation model and show improvements in system combination for Machine Translation. RWTH participated in the Italian-to-English and Chinese-to-English translation directions.

2006

pdf bib
CDER: Efficient MT Evaluation Using Block Movements
Gregor Leusch | Nicola Ueffing | Hermann Ney
11th Conference of the European Chapter of the Association for Computational Linguistics

2005

pdf bib
Preprocessing and Normalization for Automatic Evaluation of Machine Translation
Gregor Leusch | Nicola Ueffing | David Vilar | Hermann Ney
Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization

pdf bib
Evaluating Machine Translation Output with Automatic Sentence Segmentation
Evgeny Matusov | Gregor Leusch | Oliver Bender | Hermann Ney
Proceedings of the Second International Workshop on Spoken Language Translation

2003

pdf bib
A novel string-to-string distance measure with applications to machine translation evaluation
Gregor Leusch | Nicola Ueffing | Hermann Ney
Proceedings of Machine Translation Summit IX: Papers

We introduce a string-to-string distance measure which extends the edit distance by block transpositions as constant cost edit operation. An algorithm for the calculation of this distance measure in polynomial time is presented. We then demonstrate how this distance measure can be used as an evaluation criterion in machine translation. The correlation between this evaluation criterion and human judgment is systematically compared with that of other automatic evaluation measures on two translation tasks. In general, like other automatic evaluation measures, the criterion shows low correlation at sentence level, but good correlation at system level.

2000

pdf bib
An Evaluation Tool for Machine Translation: Fast Evaluation for MT Research
Sonja Nießen | Franz Josef Och | Gregor Leusch | Hermann Ney
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)