This work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2015. We participated in the MT and SLT tracks for the German→English language pair. We employ our state-of-the-art phrase-based and hierarchical phrase-based baseline systems for the MT track. ...

We compare and contrast the strengths and weaknesses of a syntax-based machine translation model with a phrase-based machine translation model on several levels. We briefly describe each model, highlighting points where they differ. We include a quantitative comparison of the phrase pairs that each model has to work with, as well as the reasons why some phrase pairs are not learned by the synta...

This report describes the results of experiments to determine the optimal training and phrase-extraction settings for HMM-based phrase tables. These were motivated by the implementation of new options for HMM training (bilexical conditioning, corrected end-distribution semantics, etc.) as well as the elimination of the “insect”, which had adversely affected phrase extraction by adding phrase pa...

Recognizing Textual Entailment (RTE) is among the most fundamental tasks in natural language processing applications, such as question answering and machine translation. One of main challenges logic-based approaches to this task lack background knowledge. This study proposes a logical inference system with phrasal knowledge by comparing their visual representations based on intuition that enabl...

Software vulnerabilities, once disclosed, can be documented in vulnerability databases, which have great potential to advance analysis and security research. People describe the key characteristics of software vulnerabilities natural language mixed with domain-specific names concepts. This textual nature poses a significant challenge for automatic knowledge embedded text. Automatic extraction a...