This paper investigates cross-lingual textual entailment as a semantic relation between two text portions in different languages , and proposes a prospective research direction . We argue that cross-lingual textual entailment ( CLTE ) can be a core technology for several cross-lingual NLP applications and tasks . Through preliminary experiments , we aim at proving the feasibility of the task , and providing a reliable baseline . We also introduce new applications for CLTE that will be explored in future work . Textual Entailment ( TE ) ( Dagan and Glickman , 2004 ) has been proposed as a generic framework for modeling language variability . Given two texts T and H , the task consists in deciding if the meaning of H can be inferred from the meaning of T. So far , TE has been only applied in a monolingual setting , where both texts are assumed to be written in the same language . In this work , we propose and investigate a cross-lingual extension of TE , where we assume that T and H are written in different languages . The great potential of integrating ( monolingual ) TE recognition components into NLP architectures has been reported in several works , such as question answering ( Harabagiu and Hickl , 2006 ) , information retrieval ( Clinchant et al . , 2006 ) , information extraction ( Romano et al . , 2006 ) , and document summarization ( Lloret et al . , 2008 ) . To the best of our knowledge , mainly due to the absence of cross-lingual TE ( CLTE ) recognition components , similar improvements have not been achieved yet in any cross-lingual application . As a matter of fact , despite the great deal of attention that TE has received in recent years ( also witnessed by five editions of the Recognizing Textual Entailment Challenge1 ) , interest for cross-lingual extensions has not been in the mainstream of TE research , which until now has been mainly focused on the English language . Nevertheless , the strong interest towards crosslingual NLP applications ( both from the market and research perspectives , as demonstrated by successful evaluation campaigns such as CLEF2 ) is , to our view , a good reason to start investigating CLTE , as well . Along such direction , research can now benefit from recent advances in other fields , especially machine translation ( MT ) , and the availability of : i ) large amounts of parallel and comparable corpora in many languages , ii ) open source software to compute word-alignments from parallel corpora , and iii ) open source software to set-up strong MT baseline systems . We strongly believe that all these resources can potentially help in developing inference mechanisms on multilingual data . Building on these considerations , this paper aims to put the basis for future research on the crosslingual Textual Entailment task , in order to allow for semantic inference across languages in different NLP tasks . Among these , as a long-term goal , we plan to adopt CLTE to support the alignment of text portions that express the same meaning in different languages . As a possible application scenario , CLTE can be used to address content merging tasks in tidy multilingual environments , such as commercial Web sites , digital libraries , or user generated content collections . Within such framework , as it will be discussed in the last section of this paper , CLTE components can be used for automatic content synchronization in a concurrent , collaborative , and multilingual editing setting , e.g. Wikipedia . This paper presented a preliminary investigation towards cross-lingual Textual Entailment , focusing on possible research directions and alternative methodologies . Baseline results have been provided to demonstrate the potentialities of a simple approach that integrates MT and monolingual TE components . Overall , our work sets a novel framework for further studies and experiments to improve crosslingual NLP tasks . In particular , CLTE can be scaled to more complex problems , such as cross-lingual content merging and synchronization .

Challenge: Regardless of the potential applications such as machine translation, the textual entailment problem has been investigated monolingually in English but not in cross-lingual setups.
Approach: They propose a cross-lingual extension of textual entailment where T and H are in different languages to show possible research directions and alternative methodologies.
Outcome: They provide the feasibility of the task and a reliable baseline through experiments to show the potential of the task.