The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Paraphrasing with bilingual parallel corpora
Open accessAbstract
Previous work has used monolingual parallel corpora to extract and generate paraphrases. We show that this task can be done using bilingual parallel corpora, a much more commonly available resource. Using alignment techniques from phrase-based statistical machine translation, we show how paraphrases in one language can be identified using a phrase in another language as a pivot. We define a paraphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to take contextual information into account. We evaluate our paraphrase extraction and ranking methods using a set of manual word alignments, and contrast the quality with paraphrases extracted from automatic alignments.
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BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
The mathematics of statistical machine translation: parameter estimation
Brown, P. F., Pietra, V. J. D., Pietra, S. A. D., & Mercer, R. L. (1993) · Cited by 3,973
A Systematic Comparison of Various Statistical Alignment Models
Och, F. J., & Ney, H. (2003) · Computational Linguistics · doi:10.1162/089120103321337421 · Cited by 3,854
Statistical phrase-based translation
Koehn, P., Och, F. J., & Marcu, D. (2003) · Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03 · doi:10.3115/1073445.1073462 · Cited by 3,197
Statistical Phrase-Based Translation
Koehn, P., Och, F. J., & Marcu, D. (2003) · doi:10.21236/ada461156 · Cited by 965
Pharaoh: A Beam Search Decoder for Phrase-Based Statistical Machine Translation Models
Koehn, P. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30194-3_13 · Cited by 628
DIRT @SBT@discovery of inference rules from text
Lin, D., & Pantel, P. (2001) · Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/502512.502559 · Cited by 541
Extracting paraphrases from a parallel corpus
Barzilay, R., & McKeown, K. R. (2001) · Proceedings of the 39th Annual Meeting on Association for Computational Linguistics - ACL '01 · doi:10.3115/1073012.1073020 · Cited by 470
A phrase-based, joint probability model for statistical machine translation
Marcu, D., & Wong, W. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118711 · Cited by 470
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
Barzilay, R., & Lee, L. (2003) · Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03 · doi:10.3115/1073445.1073448 · Cited by 426
Cited by
BLEURT: Learning Robust Metrics for Text Generation
Sellam, T., Das, D., & Parikh, A. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.704 · Cited by 1,005
Gatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776
Improving Vector Space Word Representations Using Multilingual Correlation
Faruqui, M., & Dyer, C. (2014) · Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-1049 · Cited by 620
Tenney, I., Wexler, J., Bastings, J., Bolukbasi, T., Coenen, A., Gehrmann, S., Jiang, E., Pushkarna, M., Radebaugh, C., Reif, E., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.15 · Cited by 138
“…based on datapoints of interest either manually through edits, or with a range of automatic counterfactual generators, such as backtrans-lation (Bannard and Callison-Burch, 2005), nearest-neighbor retrieval (Andoni and Indyk, 2006), word substitutions (“great → terrible”), or adversarial…”
BackgroundAutomatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing
Thompson, B., & Post, M. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.8 · Cited by 128
Metadata
- Authors
- Bannard, C., & Callison-Burch, C.
- Venue
- Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05
- Published
- 2005
- Pages
- 597-604
- OpenAlex
- W2143927888
- Cited by
- 533
- References
- 16
- FWCI
- 12.64
- Type
- conference paper
- Language
- en
- Open access
- green
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