Kitano, H. (1993). A Comprehensive and Practical Model of Memory-Based Machine Translation, in the proceeding. of the 1993 International Joint Conference on Artificial Intelligence.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....input This research has been supported in part by NATO Science for Stability Program Grant TU LANGUAGE. sentence, the correspondences between the source and target languages should be available to the system; however this issue has not been given enough consideration by the current EBMT systems [3, 5, 10, 11, 14]. Kitano has adopted the manual encoding of the translation rules, however this is a difficult and an error prone task for a large corpus. In this paper, we formulate this acquisition problem as a machine learning task in order to automate the process. We use example based learning techniques to ....

....Given that a translation is carried out using the rules learned, the accuracy of the output translation critically depends on the accuracy of the rules learned. We do not require an extra operation to maintain the grammaticality and the style of the output, as in Kitano s EBMT model [5]. The information necessary to maintain these issues is directly provided by the translation templates. The model that we have proposed in this paper may be integrated with an intelligent tutoring system (ITS) for second language learning. The parse tree representation in our model provides a ....

Kitano, H.: A Comprehensive and Practical Model of Memory-Based Machine Translation. In Ruzena Bajcsy (Ed.) Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann V.2 (1993) 1276-- 1282.

....for the sentence in the source language, parts of the corresponding target language sentence are constructed using structural equivalences and deviances in those matches. Following Nagoa s original proposal, several machine translation methods that utilize bilingual corpora have been studied [5, 9, 20, 21, 22, 23]. Some researchers [3, 24] only utilize bilingual corpora to create a bilingual dictionary and use it during the translation process. In other words, they aligned bilingual corpora at word level to figure out corresponding words in languages. Bilingual corpora is also aligned at phrase level by ....

....of human learning. The characteristic examples stored in the memory are called exemplars. In EBMT, translation examples should be available prior to the translation of an input sentence. In most of the EBMT systems, these translation examples are directly used without any generalization. Kitano [9] manually encoded translation rules, however this is a difficult and error prone task for a large corpus. In this paper, we formulate the acquisition of translation rules, which are similar to exemplars, as a machine learning problem in order to automate this task. Inductive Logic Programming ....

Kitano, H., A Comprehensive and Practical Model of Memory-Based Machine Translation, in: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, 1993, pp:1276-1282.

....for the sentence in the source language, parts of the corresponding target language sentence are constructed using structural equivalences and deviances in those matches. Following Nagoa s original proposal, several machine translation methods that utilize bilingual corpora have been studied [5, 9, 16, 17, 18, 19]. Some researchers [3, 20] only utilized bilingual corpora to create a bilingual dictionary and use it during the translation process. In other words, they aligned bilingual corpora at word level to figure out corresponding words in languages. Bilingual corpora is also aligned at phrase level by ....

....of human learning. The characteristic examples stored in the memory are called exemplars. In EBMT, translation examples should be available prior to the translation of an input sentence. In most of the EBMT systems, these translation examples are directly used without any generalization. Kitano [9] manually encoded translation rules, however this is a difficult and error prone task for a large corpus. In this paper, we formulate the acquisition of translation rules, which are similar to exemplars, as a machine learning problem in order to automate this task. The translation template ....

Kitano, H., A Comprehensive and Practical Model of Memory-Based Machine Translation, in: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, 1993, pp:1276-1282.

....for the sentence in the source language, parts of the corresponding target language sentence are constructed using structural equivalences and deviances in those matches. Following Nagoa s original proposal, several machine translation methods that utilize bilingual corpora have been studied [5, 12, 22, 23, 24, 25]. Some researchers [3, 26] only utilized bilingual corpora to create a bilingual dictionary and use it during the translation process. In other words, c fl 2000 Kluwer Academic Publishers. Printed in the Netherlands. APIN66499.tex; 3 07 2000; 10:03; p.1 2 Cicekli and Guvenir they aligned ....

....past experiences or cases to understand, plan, or learn from novel situations [10, 13, 20] In EBMT, translation examples should be available prior to the translation of an input sentence. In most of the EBMT systems, these translation examples are directly used without any generalization. Kitano [12] manually encoded translation rules, however this is a difficult and error prone task for a large corpus. In this paper, we formulate the acquisition of translation rules as a machine learning problem in order to automate this task. Our first attempt was to construct parse trees between the ....

Kitano, H., A Comprehensive and Practical Model of Memory-Based Machine Translation, in: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, 1993, pp:1276-1282.

....corpus into translation patterns, which are then used as a translation memory. Preliminary results on the German English Verbmobil corpus are given. 1 Introduction In recent years, example based translation has been proposed as an ecient method for automatic translation (Sato and Nagao, 1990; Kitano, 1993; Brown, 1996) Translations are stored in a translation memory and used to construct translations for new sentences. In its simplest version, example based translation boils down to using a database of source sentences with their translations. For many translation tasks, especially in computer ....

H. Kitano. 1993. A comprehensive and practical model of memory-based machine translation.

....between the source and target languages should be available to the system; however this issue has not been given enough consideration by the current EBMT systems. Kitano has adopted the manual encoding of the translation rules, however this is a dicult and an errorprone task for a large corpus [8]. Sato [18] also proposed an exemplar based system with manually encoded matching expressions which are used as translation templates. This paper formulates the acquisition of translation rules as a machine learning task in order to automate the process. Our rst attempt was to construct parse ....

....Given that a translation is carried out using the rules learned, the accuracy of the output translation critically depends on the accuracy of the rules learned. We do not require an extra operation to maintain the grammaticality and the style of the output, as in Kitano s EBMT model [8]. The information necessary to maintain these issues is directly provided by the translation templates. The learning and translation times on the small training set are quite reasonable, and that indicates the program will scale up real large training corpora. Note that this algorithm is not speci ....

Hiroaki Kitano. A comprehensive and practical model of memory-based machine translation. In IJCAI-93 (1993).

....and Steinbuch, 1963; Batchelor, 1974) These machines calculated either a Manhattan or Euclidean distance for all stored points, and then did comparisons to pick the winning point. The current version of this technology is the wafer scale memory based reasoning devices proposed by Yasunaga and Kitano (1993). The devices allocate one processor per data point, and can handle approximately 1.7 million data points per 8 inch wafer. The designers have exploited the properties of memory based learning in two ways. First, the resolution of the computed distance is not critical, so analog adders and ....

....fields, as that is the operation the associative memory chips can support. Future associative memories might implement Euclidean distance as a basic operation. There have been implementations of memory based translation and parsing on the IXM2 (Kitano and Higuchi, 1991a,b; Sumita et al. 1993; Kitano, 1993a,b) The current generic parallel computer seems to be on the order of 100 standard microprocessors tightly connected with a communication network. Examples of this design are the CM5 and the SNAP system (Kitano et al. 1991) The details of the communication network are not critical to locally ....

Kitano, H. (1993b). A comprehensive and practical model of memory-based machine translation. In IJCAI 13 (1993), pages 1276--1282.

.... This research has been supported in part by NATO Science for Stability Program Grant TU LANGUAGE. sentence, the correspondences between the source and target languages should be available to the system; however this issue has not been given enough consideration by the current EBMT systems [3, 5, 10, 11, 14]. Kitano has adopted the manual encoding of the translation rules, however this is a difficult and an error prone task for a large corpus. In this paper, we formulate this acquisition problem as a machine learning task in order to automate the process. We use example based learning techniques to ....

....Given that a translation is carried out using the rules learned, the accuracy of the output translation critically depends on the accuracy of the rules learned. We do not require an extra operation to maintain the grammaticality and the style of the output, as in Kitano s EBMT model [5]. The information necessary to maintain these issues is directly provided by the translation templates. The model that we have proposed in this paper may be integrated with an intelligent tutoring system (ITS) for second language learning. The parse tree representation in our model provides a ....

Kitano, H.: A Comprehensive and Practical Model of Memory-Based Machine Translation. In Ruzena Bajcsy (Ed.) Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann V.2 (1993) 1276-- 1282.

....between the source and target languages should be available to the system; however this issue has not been given enough consideration by the current EBMT systems. Kitano has adopted the manual encoding of the translation rules, however this is a difficult and an error prone task for a large corpus [5]. In this paper, we formulate this acquisition problem as a machine learning task in order to automate the process. In this paper, we propose a technique which stores exemplars in the form of templates that are generalized exemplars. A template is an example translation pair where some components ....

....Given that a translation is carried out using the rules learned, the accuracy of the output translation critically depends on the accuracy of the rules learned. We do not require an extra operation to maintain the grammaticality and the style of the output, as in Kitano s EBMT model [5]. The information necessary to maintain these issues is directly provided by the translation templates. The model that we have proposed in this paper may be integrated with an intelligent tutoring system (ITS) for second language learning. The template representation in our model provides a level ....

Kitano, H.: A Comprehensive and Practical Model of Memory-Based Machine Translation. In Ruzena Bajcsy (Ed.) Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann V.2 (1993) 12761282.

....representation. The characteristic examples stored in the memory are called exemplars. In the translation process, providing the correspondences between the source and target languages is a very difficult task in EBMT. Although, manual encoding of the translation rules has been achieved by Kitano [10], when the corpus is very large, it becomes a complicated and error prone task. Therefore Cicekli and G uvenir [7, 4] offered a technique in which the problem is taken as a machine learning task. Exemplars are stored in the form of templates that are generalized exemplars. A template is an example ....

H. Kitano. A Comprehensive and Practical Model of Memory-Based Machine Translation. In Ruzena Bajcsy (Ed.) Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann Volume 2, 1993, pp: 1276-1282.

....the correspondences between the source and target languages This research has been supported in part by NATO Science for Stability Program Grant TULANGUAGE. should be available to the system; however this issue has not been given enough consideration by the current EBMT systems. Kitano [5] has adopted the manual encoding of the translation rules, however this is a difficult and an error prone task for a large corpus. Wu [16] uses a method to extract phrasal translation examples in sentence aligned parallel corpora using a probabilistic translation lexicon for the language pair. ....

....Given that a translation is carried out using the rules learned, the accuracy of the output translation critically depends on the accuracy of the rules learned. We do not require an extra operation to maintain the grammaticality and the style of the output, as in Kitano s EBMT model [5]. The information necessary to maintain these issues is directly provided by the translation rules. The model that we have proposed in this paper may be integrated with an intelligent tutoring system (ITS) for second language learning. The rule representation in our model provides a level of ....

Kitano, H.: A Comprehensive and Practical Model of Memory-Based Machine Translation. In Ruzena Bajcsy (Ed.) Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann V.2 (1993) 1276-1282.

....between the source and target languages should be available to the system; however this issue has not been given enough consideration by the current EBMT systems. Kitano has adopted the manual encoding of the translation rules, however this is a difficult and an error prone task for a large corpus [6]. This paper formulates the acquisition problem as a machine learning task in order to automate the process. Our first attempt was to construct parse trees between the example translation pairs [4] However, the difficulty was the availability of a reliable parser for both languages. In this ....

....Given that a translation is carried out using the rules learned, the accuracy of the output translation critically depends on the accuracy of the rules learned. We do not require an extra operation to maintain the grammaticality and the style of the output, as in Kitano s EBMT model [6]. The information necessary to maintain these issues is directly provided by the translation templates. The learning and translation times on the small training set are quite reasonable, and that indicates the program will scale up real large training corpora. Note that this algorithm is not ....

Kitano, H.: A Comprehensive and Practical Model of Memory-Based Machine Translation. In Ruzena Bajcsy (Ed.) Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann V.2 (1993) 1276-1282.

....between the source and target languages should be available to the system; however this issue has not been given enough consideration by the current EBMT systems. Kitano has adopted the manual encoding of the translation rules, however this is a difficult and an errorprone task for a large corpus [8]. Sato [18] also proposed an exemplar based system with manually encoded matching expressions which are used as translation templates. This paper formulates the acquisition of translation rules as a machine learning task in order to automate the process. Our first attempt was to construct parse ....

....Given that a translation is carried out using the rules learned, the accuracy of the output translation critically depends on the accuracy of the rules learned. We do not require an extra operation to maintain the grammaticality and the style of the output, as in Kitano s EBMT model [8]. The information necessary to maintain these issues is directly provided by the translation templates. The learning and translation times on the small training set are quite reasonable, and that indicates the program will scale up real large training corpora. Note that this algorithm is not ....

H. Kitano. A comprehensive and practical model of memory-based machine translation. In 13. IJCAI, Chamb'ery, France (1993).

No context found.

Kitano, H. (1993). A Comprehensive and Practical Model of Memory-Based Machine Translation, in the proceeding. of the 1993 International Joint Conference on Artificial Intelligence.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC