Using Machine Translation Systems to Expand a Corpus in Textual Entailment

Castillo, Julio Javier

doi:10.1007/978-3-642-14770-8_12

Cited by 8 publications

(7 citation statements)

References 4 publications

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“…It also gives us a way to create a bilingual corpus taking advantage of the latest advances in MT, and allows us to obtain interesting paraphrases of H by using different MT systems, and thus provide greater semantic variability which is useful for the textual entailment inference [10]. In addition, this procedure allow us to create a bilingual corpus in any language pairs.…”

Section: Training Setmentioning

confidence: 99%

“…Additionally, we generated the following training sets: RTE3-4C, 2 and RTE4-4C (see footnote 2) as proposed by Castillo [10] in order to extend the RTE data sets by using machine translation engines. Thus, we analyze ten WordNet-based measures with the purpose of obtaining the maximum similarity between two concepts.…”

Section: System Overviewmentioning

confidence: 99%

“…In this section, we used the algorithm proposed by Castillo [10] to generate additional training data, in other words to ''expand a data set'', starting with RTE3 and RTE4 followed by a Double Translation Process (dtp) or round-trip translation. Double translation process can be defined as the process of starting with an S (String in English), translating it to a foreign language F(S), for example Spanish, and finally back into the English source language F -1 (S).…”

Section: Monolingual Textual Entailmentmentioning

confidence: 99%

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A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment

Castillo

2011

Int. J. Mach. Learn. & Cyber.

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In this paper we explain how to build a recognizing textual entailment (RTE) system which only uses semantic similarity measures based on WordNet. We show how the widely used WordNet-based semantic measures can be generalized to build sentence level semantic metrics in order to be used in both mono-lingual and cross-lingual textual entailment. We experiment with a wide variety of RTE datasets and evaluate the contribution of an algorithm which expands the RTE monolingual corpus. Results achieved with this method yielded significant statistical differences when predicting RTE test sets. We provide an efficiency analysis of these metrics drawing some conclusions about their practical utility in recognizing textual entailment. We also analyze the cross-lingual textual entailment task, we create a bilingual English-Spanish corpus, and propose a procedure to create a cross-lingual textual entailment corpus for any pair of languages. Finally, we show that the proposed method is enough to build an average score RTE system in both monolingual and cross-lingual textual entailment, that uses semantic information from WordNet as the only source of lexicalsemantic knowledge.

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Section: Training Setmentioning

confidence: 99%

Section: System Overviewmentioning

confidence: 99%

Section: Monolingual Textual Entailmentmentioning

confidence: 99%

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A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment

Castillo

2011

Int. J. Mach. Learn. & Cyber.

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“…We used the following training sets: RTE3-4C 4 , and RTE4-4C 4 , as proposed by the authors in [9] in order to extend the 4 http://www.investigacion.frc.utn.edu.ar/mslabs/~jcastillo/Sagan-test-suite/ RTE data sets by using machine translation engines following a variation of the round trip translation technique. We remark that all corpus used in this paper are available to the community 4 .…”

Section: System Architecturementioning

confidence: 99%

“…The sign "+" represents the union operation of sets and "4C" means "four combinations" denoting that the dataset was generated using the algorithm to expand datasets [9] and using only one Translator engine.…”

Section: System Architecturementioning

confidence: 99%

An Approach to Cross-Lingual Textual Entailment using Online Machine Translation Systems

Castillo¹,

Cardenas²

2011

Polibits

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In this paper, we show an approach to cross-lingual textual entailment (CLTE) by using machine translation systems such as Bing Translator and Google Translate. We experiment with a wide variety of data sets to the task of textual Entailment (TE) and evaluate the contribution of an algorithm that expands a monolingual TE corpus that seems promising for the task of CLTE. We built a CLTE corpus and we report a procedure that can be used to create a CLTE corpus in any pair of languages. We also report the results obtained in our experiments with the three-way classification task for CLTE and we show that this result outperform the average score of RTE (Recognizing Textual Entailment) systems. Finally, we find that using WordNet as the only source of lexical-semantic knowledge it is possibly to build a system for CLTE, which achieves comparable results with the average score of RTE systems for both two-way and three-way tasks.

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A Semantic Oriented Approach to Textual Entailment Using WordNet-Based Measures

Castillo

2010

Advances in Artificial Intelligence

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Using Machine Translation Systems to Expand a Corpus in Textual Entailment

Cited by 8 publications

References 4 publications

A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment

A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment

An Approach to Cross-Lingual Textual Entailment using Online Machine Translation Systems

A Semantic Oriented Approach to Textual Entailment Using WordNet-Based Measures

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