Natural Language Engineering 1 (1): 000–000. Printed in the United Kingdom c  1998 Cambridge University Press 1 Design and Realization of a Modular Architecture for Textual Entailment Sebastian Padó Institute for Natural Language Processing, Stuttgart University 70569 Stuttgart, Germany pado@ims.uni-stuttgart.de Tae-Gil Noh Institute of Computational Linguistics, Heidelberg University 69120 Heidelberg, Germany noh@cl.uni-heidelberg.de Asher Stern Dept. of Computer Science, Bar-Ilan University Ramat Gan 52900, Israel astern7@gmail.com Rui Wang German Research Center for Artificial Intelligence 66123 Saarbrücken, Germany rui.wang@dfki.de Roberto Zanoli Human Language Technology, Fondazione Bruno Kessler 38123 Trento, Italy zanoli@fbk.eu ( Received 8 November 2013 ) Abstract A key challenge at the core of many NLP tasks is the ability to determine which conclusions can be inferred from a given natural language text. This problem, called the Recognition of Textual Entailment (RTE), has initiated the development of a range of algorithms, methods and technologies. Unfortunately, research on TE (like semantics research more generally), is fragmented into studies focussing on various aspects of semantics such as world knowledge, lexical and syntactic relations, or more specialized kinds of inference. This fragmentation has problematic practical consequences. Notably, interoperability among existing RTE systems is poor, and reuse of resources and algorithms is mostly infeasible. This also makes systematic evaluations very difficult to carry out. Finally, TE presents a wide array of approaches to potential end users with little guidance on which to pick. Our contribution to this situation is the novel EXCITEMENT architecture, which was developed to enable and encourage the consolidation of methods and resources in the TE area. It decomposes RTE into components with strongly typed interfaces. We specify (a) a modular linguistic analysis pipeline and (b) a decomposition of the “core” RTE methods