IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical methods for speech recognition
Statistical methods for speech recognition
Automata, Languages, and Machines
Automata, Languages, and Machines
The EuTrans Spoken Language Translation System
Machine Translation
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improve the Learning of Subsequential Transducers by Using Alignments and Dictionaries
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Inference of Finite-State Transducers by Using Regular Grammars and Morphisms
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Finite-State Speech-to-Speech Translation
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Acoustic and syntactical modeling in the ATROS system
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Stochastic finite-state models for spoken language machine translation
EmbedMT '00 ANLP-NAACL 2000 Workshop: Embedded Machine Translation Systems
Probabilistic Finite-State Machines-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Translation with Inferred Stochastic Finite-State Transducers
Computational Linguistics
Learning finite-state models for machine translation
Machine Learning
ON THE STATISTICAL ESTIMATION OF STOCHASTIC FINITE-STATE TRANSDUCERS IN MACHINE TRANSLATION
Applied Artificial Intelligence
Statistical framework for a Spanish spoken dialogue corpus
Speech Communication
Statistical approaches to computer-assisted translation
Computational Linguistics
A study of a segmentation technique for dialogue act assignation
IWCS-8 '09 Proceedings of the Eighth International Conference on Computational Semantics
CLAGI '09 Proceedings of the EACL 2009 Workshop on Computational Linguistic Aspects of Grammatical Inference
Learning finite state transducers using bilingual phrases
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Active learning for dialogue act labelling
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Information extraction from semi-structured resources: a two-phase finite state transducers approach
CIAA'11 Proceedings of the 16th international conference on Implementation and application of automata
On multimodal interactive machine translation using speech recognition
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Automatic annotation of dialogues using n-grams
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Weighted finite-state transducer inference for limited-domain speech-to-speech translation
PROPOR'06 Proceedings of the 7th international conference on Computational Processing of the Portuguese Language
Towards a database for genotype-phenotype association research: mining data from encyclopaedia
International Journal of Data Mining and Bioinformatics
| Hi-index | 0.01 |
Finite-state transducers are models that are being used in different areas of pattern recognition and computational linguistics. One of these areas is machine translation, where the approaches that are based on building models automatically from training examples are becoming more and more attractive. Finite-state transducers are very adequate to be used in constrained tasks where training samples of pairs of sentences are available. A technique to infer finite-state transducers is proposed in this work. This technique is based on formal relations between finite-state transducers and finite-state grammars. Given a training corpus of input-output pairs of sentences, the proposed approach uses statistical alignment methods to produce a set of conventional strings from which a stochastic finite-state grammar is inferred. This grammar is finally transformed into a resulting finite-state transducer. The proposed methods are assessed through series of machine translation experiments within the framework of the EUTRANS project.