Abstract
Spoken Language Translation (SLT) is the research area that focuses on the translation of speech or text between two spoken languages. Phrase-based and syntax-based methods represent the state-of-the-art for statistical machine translation (SMT). The phrase-based method specializes in modeling local reorderings and translations of multiword expressions. The syntax-based method is enhanced by using syntactic knowledge, which can better model long word reorderings, discontinuous phrases, and syntactic structure. In this article, we leverage on the strength of these two methods and propose a strategy based on multiple hypotheses generation in a two-stage framework for spoken language translation. The hypotheses are generated in two stages, namely, decoding and regeneration. In the decoding stage, we apply state-of-the-art, phrase-based, and syntax-based methods to generate basic translation hypotheses. Then in the regeneration stage, much more hypotheses that cannot be captured by the decoding algorithms are produced from the basic hypotheses. We study three regeneration methods: redecoding, n-gram expansion, and confusion network in the second stage. Finally, an additional reranking pass is introduced to select the translation outputs by a linear combination of rescoring models. Experimental results on the Chinese-to-English IWSLT-2006 challenge task of translating the transcription of spontaneous speech show that the proposed mechanism achieves significant improvements over the baseline of about 2.80 BLEU-score.
- Aiello, D., Cerrato, L., Delogu, C., and Di Carlo, A. 1999. EUTRANS project: FUB activity in spoken machine translation. In Proceedings of the Venezia per il Trattamento Automatico delle Lingue (VEXTAL’99).Google Scholar
- Amengual, J. C., Castano, A., Castellanos, A., Jimenez, V. M., Llorens, D., Marzal A., Prat, F., Vilar, J. M., Benedi, J. M., Casacuberta, F., Pastor, M., and Vidal, E. 2000. The EuTrans Spoken Language Translation System. J. Mach. Transl. 15, 75--103. Google ScholarDigital Library
- Bangalore, S., Bordel, G., and Riccardi, G. 2001. Computing consensus translation from multiple machine translation systems. In Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU’01), 351--354.Google Scholar
- Bangalore, S. and Riccardi, G. 2002. Stochastic finite-state models for spoken language machine translation. J. Mach. Transl. 17, 3, 165--184. Google ScholarDigital Library
- Berger, A. L., Brown, P. F., Della Pietra, S. A., Della Pietra, V. J., Kehler, A. S., and Mercer, R. L. 1996. Language translation apparatus and methods using context-based translation models. U.S. Patent No. 5,510,981.Google Scholar
- Besacier, L., Blanchon, H., Fouquet, Y., Guilbaud, J. P., Helme, S., Mazenot, S., Moraru, D., and Vaufreydaz, D. 2001. Speech translation for French in the NESPOLE! European project. In Proceedings of the European Conference on Speech Communication and Technology (EUROSPEECH’01).Google Scholar
- Brown, P. F., Della Pietra, V. J., Della Pietra, S. A., and Mercer, R. L. 1993. The mathematics of statistical machine translation: Parameter estimation. Comput. Linguist. 192, 263--312. Google ScholarDigital Library
- Casacuberta, F., Vidal, E., and Vilar, J. M. 2002. Architectures for speech-to-speech translation using finite-state models. In Proceedings of the Speech-to-Speech Translation Workshop, 39--44. Google ScholarDigital Library
- Chen, B., Cattoni, R., Bertoldi, N., Cettolo, M., and Federico, M. 2005. The ITC-irst SMT System for IWSLT-2005. In Proceedings of the International Workshop for Spoken Language Translation (IWSLT’05), 98--104.Google Scholar
- Chen, B., Cettolo, M., and Federico, M. 2006. Reordering rules for phrase-based statistical machine translation. In Proceedings of the International Workshop for Spoken Language Translation (IWSLT’06).Google Scholar
- Chen, B., Federico, M., and Cettolo, M. 2007a. Better N-best translations through generative n-gram language models. In Proceedings of the Machine Translation Summit XI (MT’07).Google Scholar
- Chen, B., Sun, J., Jiang, H., Zhang M., and Aw, A. T. 2007b. I2R Chinese-English translation system for IWSLT-2007. In Proceedings of the International Workshop for Spoken Language Translation (IWSLT’07).Google Scholar
- Chen, B., Zhang M., Aw, A. T., and Li, H. 2008. Regenerating hypotheses for statistical machine translation. In Proceedings of the International Conference on Computer Linguistics (COLING’08). Google ScholarDigital Library
- Chen, S. F. and Goodman, J. 1998. An empirical study of smoothing techniques for language modeling. Tech. rep. TR-10-98, Center for Research in Computing Technology, Harvard University.Google Scholar
- Doddington, G. 2002. Automatic evaluation of machine translation quality using N-gram co-occurrence statistics. In Proceedings of the Human Language Technology Conference/North American Chapter of the Association for Computational Linguistics (HLT-NAACL’02). Google ScholarDigital Library
- Eurospeech. 2003. Special session: Multilingual speech-to-speech translation. In Proceedings of the 8th European Conference on Speech Communication and Technology (EUROSPEECH’03), 361--384.Google Scholar
- Fellbaum, C. Ed. 1998. WordNet: An Electronic Lexical Database. MIT Press.Google Scholar
- Fiscus, J. G. 1997. A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (ROVER). In Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU’97), 347--352.Google ScholarCross Ref
- Frederking, R. and Nirenburg, S. 1994. Three heads are better than one. In Proceedings of the 4th ACL Conference on Applied Natural Language Processing (ANLP’94), 95--100. Google ScholarDigital Library
- Frederking, R., Rudnicky, A., and Hogan, C. 1997. Interactive speech translation in the DIPLOMAT project. In Proceedings of the Workshop on Spoken Language Translation (IWSLT’97).Google Scholar
- Gao, Y., Zhou, B., Diao, Z., Sorensen, J., and Picheny, M. 2002. MARS: A statistical semantic parsing and generation-based multilingual automatic Translation system. Mach. Transl. 17, 3, 185--212. Google ScholarDigital Library
- He, Z., Mi, H., Liu, Y., Xiong, D., Luo, W., Huang, Y., Ren, Z., Lu, Y., and Liu, Q. 2007. The ICT statistical machine translation systems for IWSLT 2007. In Proceedings of the International Workshop for Spoken Language Translation (IWSLT’07).Google Scholar
- Hitoshi, I., Sumita, E., and Furuse, O. 1996. Spoken language translation method using examples. In Proceedings of the International Conference on Computer Linguistics (COLING’96). Google ScholarDigital Library
- Hoge, H. 2002. Project proposal TC-STAR: Make speech-to-speech translation real. In Proceedings of the 3rd International Conference on Language Resources and Evaluation (LREC’02), 136--141.Google Scholar
- Horiguchi K. and Franz, A. 1997. A formal basis for spoken language translation by analogy. In Proceedings of the Workshop Spoken Language Translation in Conjunction with ACL/EACL’97 (IWSLT’97), 32--39.Google Scholar
- Hovy, E. 1994. PANGLOSS: Knowledge-based machine translation. In Proceedings of the Workshop on Human Language Technology (HLT’94), 478--478. Google ScholarDigital Library
- Hsiao, R., Venugopal, A., Kohler, T. Zhang, Y., Zollmann, P. C. A., Vogel, S., Black, A. W., Schultz, T., and Waibel, A. 2006. Optimizing components for handheld two-way speech translation for an English-Iraqi Arabic system. In Proceedings of the International Conference on Spoken Language Processing (ICSLP’06).Google Scholar
- Huang, F. and Papineni, K. 2007. Hierarchical system combination for machine translation. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL’07), 277--286.Google Scholar
- Koehn, P. 2004. Statistical significance tests for machine translation evaluation. In Proceedings of Joint Conference on Empirical Methods in Natural Language Processing (EMNLP’04), 388--395.Google Scholar
- Koehn, P., Axelrod, A., Mayne, A. B., Callison-Burch, C., Osborne, M., and Talbot, D. 2005. Edinburgh system description for the 2005 IWSLT speech translation evaluation. In Proceedings of the Workshop Spoken Language Translation (IWSLT’05).Google Scholar
- Koehn, P., Och, F. J., and Marcu, D. 2003. Statistical phrase-based translation. In Proceedings of Human Language Technology Conference/North American Chapter on the Association for Computational Linguistics (HLT/NAACL’03), 127--133. Google ScholarDigital Library
- Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., Dyer, C., Bojar, O., Constantin, A., and Herbst, E. 2007. Moses: Open source toolkit for statistical machine translation. In Proceedings of the Association of Computer Linguistics (ACL’07), 177--180. Google ScholarDigital Library
- Kraif, O. and Chen, B. 2004. Combining clues for lexical level aligning using the Null hypothesis a roach. In Proceedings of the International Conference on Computer Linguistics (COLING’04), 1261--1264. Google ScholarDigital Library
- Kumar, S. and Byrne, W. 2004. Minimum Bayes-risk decoding for statistical machine translation. In Proceedings of the Workshop on Human Language Technology (HLT’04).Google Scholar
- Lavie, A., Langley, C., Waibel, A., Pianesi, F., Lazzari, G., Coletti, P., Taddei, L., and Balducci, F. 2001. Architecture and design considerations in NESPOLE! A speech translation system for e-commerce applications. In Proceedings of the 1st International Conference on Human Language Technology Research (HLT’01), J. Allan, Ed., 31--39. Google ScholarDigital Library
- Lee, Y.-S., Yi, W. S., Seneff, S., and Weinstein, C. 2001. Interlingua-based broad-coverage Korean-to-English translation in CCLINC. In Proceedings of the 1st International Conference on Human Language Technology Research (HLT’01). Google ScholarDigital Library
- Macherey, W. and Och, F. J. 2007. An empirical study on computing consensus translations from multiple machine translation systems. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL’07), 986--995.Google Scholar
- Matusov, E., Ueffing, N., and Ney, H. 2006. Computing consensus translation from multiple machine translation systems using enhanced hypotheses alignment. In Proceedings of the Conference on the European Chapter of the Association for Computational Linguistic (EACL’06).Google Scholar
- Melamed, I. D. 2000. Models of translational equivalence among words. Comput. Linguist. 262, 221--249. Google ScholarDigital Library
- Ney, H., Nieben, S., Och, F. J., Sawaf, H., Tillmann, C., and Vogel, S. 2000. Algorithms for statistical translation of spoken language. In IEEE Trans. Speech Audio Process. 8, 1, 24--36.Google ScholarCross Ref
- Ney, H. 2003. The statistical approach to machine translation and a roadmap for speech translation. In Proceedings of European Conference on Speech Communication and Technology (EUROSPEECH’03), 361--364.Google Scholar
- Nirenburg, S., Carbonell, J., Tomita, M., and Goodman, K. 1992. Machine Translation: A Knowledge-Based Approach. Morgan Kaufmann Publishers, San Mateo, CA. Google ScholarDigital Library
- Och, F. J. 2003. Minimum error rate training in statistical machine translation. In Proceedings of the Association of Computer Linguistics (ACL’03). Google ScholarDigital Library
- Och, F. J. and Ney, H. 2002. Discriminative training and maximum entropy models for statistical machine translation. In Proceedings of the Association of Computer Linguistics (ACL’02). Google ScholarDigital Library
- Och, F. J. and Ney, H. 2003. A systematic comparison of various statistical alignment models. Comput. Linguist. 291, 19--51. Google ScholarDigital Library
- Papineni, K., Roukos, S., Ward, T., and Zhu, W. J. 2002. BLEU: A method for automatic evaluation of machine translation. In Proceedings of the Association of Computer Linguistics (ACL’02). Google ScholarDigital Library
- Paul, M., Doi, T., Hwang, Y., Imamura, K., Okuma, H., and Sumita, E. 2005. Nobody is perfect: ATR’s hybrid a roach to spoken language translation. In Proceedings of the Workshop Spoken Language Translation (IWSLT’05), 55--62.Google Scholar
- Quirk, C. and Menezes, A. 2006. Do we need phrases? Challenging the conventional wisdom in SMT. In Proceedings of the International Conference on Computer Linguistics (COLING’06), 9--16. Google ScholarDigital Library
- Rayner, M. and Bouillon, P. 1995. Hybrid transfer in an English-French spoken language translator. In Proceedings of IA’95, 153--162.Google Scholar
- Rayner, M. and Carter, D. 1997. Hybrid language processing in the spoken language translator. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’97). Google ScholarDigital Library
- Rosti, A., Ayan, N. F., Xiang, B., Matsoukas, S., Schwartz, R., and Dorr, B. 2007a. Combining outputs from multiple machine translation systems. In Proceedings of the Human Language Technology Conference/North American Chapter of the Association for Computational Linguistics (HLT-NAACL’03), 228--235.Google Scholar
- Rosti, A., Matsoukas, S., and Schwartz, R. 2007b. Improved word-level system combination for machine translation. In Proceedings of the Association of Computer Linguistics (ACL’07).Google Scholar
- Sebastian, S., Zong, C., Reichert, J., Cao, W., Kolss, M., Xie, G., Peterson, K., Ding, P., Arranz, V., Yu, J., and Waibel, A. 2006. Speech-to-speech translation services for the Olympic games 2008. In Proceedings of the 3rd Joint Workshop on Machine Learning and Multimodal Interaction (MLMI’06).Google Scholar
- Seligman, M. 2000. Nine issues in speech translation. Machine Translation 15, 149--185. Google ScholarDigital Library
- Sim, K. C., Byrne, W. J., Gales, M. J. F., Sahbi, H., and Woodland, P. C. 2007. Consensus network decoding for statistical machine translation system combination. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’07).Google Scholar
- Snover, M., Dorr, B., Schwartz, R., Micciulla, L., and Makhoul, J. 2006. A study of translation edit rate with targeted human annotation. In Proceedings of Association for Machine Translation in the Americas (AMTA’06).Google Scholar
- Stolcke, A. 2002. SRILM -- An extensible language modeling toolkit. In Proceedings of the International Conference on Spoken Language (ICSLP’02), 901--904.Google Scholar
- Sugaya, F., Takezawa, T., Yokoo, A., Sagisaka, Y., and Yamamoto, S. 1999. End-to-end evaluation in ATR-MATRIX: Speech translation system between English and Japanese. In Proceedings of European Conference on Speech Communication and Technology (EUROSPEECH’99), 2431--2434.Google Scholar
- Sumita, E., Yamada, S., Yamamoto, K., Paul, M., Kashioka, H., Ishikawa, K., and Shirai S. 1999. Solutions to problems inherent in spoken-language translation: The ATR-MATRIX a roach. In Proceedings of Machine Translation Summit VII (MT’99), 229--235.Google Scholar
- Sumita, E., Akiba, Y., and Doi, T. 2003. A corpus-centered a roach to spoken language translation. In Proceedings of the Conference of the Association for Computational Linguistics (ACL’03), 171--174. Google ScholarDigital Library
- Takezawa, T., Sumita, E., Sugaya, F., Yamamoto, H., and Yamamoto, S. 2002. Toward a broad-coverage bilingual corpus for speech translation of travel conversations in the real world. In Proceedings of the International Conference on Language Resources and Evaluation (LREC’02).Google Scholar
- Takezawa, T., Morimoto, T., Sagisaka, Y., Campbell, N., Iida, H., Sugaya, F., Yokoo, A., and Yamamoto, S. 1998. A Japanese-to-English speech translation system: ATR-MATRIX. In Proceedings of the International Conference on Spoken Language Processing (ICSLP’98).Google Scholar
- Tillmann, C. and Zhang, T. 2005. A localized prediction model for statistical machine translation. In Proceedings of the Conference of the Association for Computational Linguistics (ACL’05), 557--564. Google ScholarDigital Library
- Ueffing, N. and Ney, H. 2007. Word-level confidence estimation for machine translation. Comput. Linguist. 331, 9--40. Google ScholarDigital Library
- Vidal, E. 1997. Finite-state speech-to-speech translation. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’97). Google ScholarDigital Library
- Vogel, S., Hewavitharana, S., Kolss, M., and Waibel, A. 2004. The ISL statistical translation system for spoken language translation. In Proceedings of the International Workshop on Spoken Language Translation (IWSLT’04).Google Scholar
- Waibel, A., Jain, A., Mcnair, A., Saito, H., Hauptmann, A., and Tebelskis, J. 1991. JANUS: A speech-to-speech translation system using connectionist and symbolic processing strategies. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’91). Google ScholarDigital Library
- Wahlster, W., Ed. 2000. Verbmobil: Foundations of Speech-to-Speech Translations. Springer Verlag, Berlin.Google Scholar
Index Terms
- Two-Stage Hypotheses Generation for Spoken Language Translation
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