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Linguistic resources for paraphrase generation in Portuguese: a Lexicon-Grammar approach
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In: ISSN: 1574-020X ; EISSN: 1574-0218 ; Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-03548861 ; Language Resources and Evaluation, Springer Verlag, 2022, ⟨10.1007/s10579-021-09561-5⟩ ; https://link.springer.com/article/10.1007/s10579-021-09561-5 (2022)
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Automatic Construction of Fine-Grained Paraphrase Corpora System Using Language Inference Model
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In: Applied Sciences; Volume 12; Issue 1; Pages: 499 (2022)
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Using Alignments in Automatic Paraphrase Production to Combat Data Sparsity in Question Interpretation for a Virtual Patient Dialogue System
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Question Paraphrase Generation for Question Answering System
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A Computational Approach to the Analysis and Generation of Emotion in Text
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A Computational Approach to the Analysis and Generation of Emotion in Text
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A Computational Approach to the Analysis and Generation of Emotion in Text ...
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A Computational Approach to the Analysis and Generation of Emotion in Text
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The Circle of Meaning: From Translation to Paraphrasing and Back
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Abstract:
The preservation of meaning between inputs and outputs is perhaps the most ambitious and, often, the most elusive goal of systems that attempt to process natural language. Nowhere is this goal of more obvious importance than for the tasks of machine translation and paraphrase generation. Preserving meaning between the input and the output is paramount for both, the monolingual vs bilingual distinction notwithstanding. In this thesis, I present a novel, symbiotic relationship between these two tasks that I term the "circle of meaning''. Today's statistical machine translation (SMT) systems require high quality human translations for parameter tuning, in addition to large bi-texts for learning the translation units. This parameter tuning usually involves generating translations at different points in the parameter space and obtaining feedback against human-authored reference translations as to how good the translations. This feedback then dictates what point in the parameter space should be explored next. To measure this feedback, it is generally considered wise to have multiple (usually 4) reference translations to avoid unfair penalization of translation hypotheses which could easily happen given the large number of ways in which a sentence can be translated from one language to another. However, this reliance on multiple reference translations creates a problem since they are labor intensive and expensive to obtain. Therefore, most current MT datasets only contain a single reference. This leads to the problem of reference sparsity---the primary open problem that I address in this dissertation---one that has a serious effect on the SMT parameter tuning process. Bannard and Callison-Burch (2005) were the first to provide a practical connection between phrase-based statistical machine translation and paraphrase generation. However, their technique is restricted to generating phrasal paraphrases. I build upon their approach and augment a phrasal paraphrase extractor into a sentential paraphraser with extremely broad coverage. The novelty in this augmentation lies in the further strengthening of the connection between statistical machine translation and paraphrase generation; whereas Bannard and Callison-Burch only relied on SMT machinery to extract phrasal paraphrase rules and stopped there, I take it a few steps further and build a full English-to-English SMT system. This system can, as expected, ``translate'' any English input sentence into a new English sentence with the same degree of meaning preservation that exists in a bilingual SMT system. In fact, being a state-of-the-art SMT system, it is able to generate n-best "translations" for any given input sentence. This sentential paraphraser, built almost entirely from existing SMT machinery, represents the first 180 degrees of the circle of meaning. To complete the circle, I describe a novel connection in the other direction. I claim that the sentential paraphraser, once built in this fashion, can provide a solution to the reference sparsity problem and, hence, be used to improve the performance a bilingual SMT system. I discuss two different instantiations of the sentential paraphraser and show several results that provide empirical validation for this connection.
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Keyword:
Artificial Intelligence; Computational Linguistics; Computer Science; Language; Linguistics; Machine Translation; Natural Language Processing; Paraphrase Generation
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URL: http://hdl.handle.net/1903/10502
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| 11 |
Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models
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A Symbolic Approach to Near-Deterministic Surface Realisation using Tree Adjoining Grammar
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In: 45th Annual Meeting of the Association for Computational Linguistics - ACL 2007 ; https://hal.inria.fr/inria-00149366 ; 45th Annual Meeting of the Association for Computational Linguistics - ACL 2007, Jun 2007, Prague, Czech Republic. pp.328-335 (2007)
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THE TEACHER MODE OF THE SENTENCE FAIRY SYSTEM: HOW TO CREATE YOUR OWN E-LEARNING WRITING LESSONS FOR GERMAN ELEMENTARY SCHOOL PUPILS
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In: http://userpages.uni-koblenz.de/~harbusch/ICERI-2012.pdf
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| 19 |
Experience
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In: http://www.desilinguist.org/pdf/madnani-cv.pdf
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Exploring neural paraphrasing to improve fluency of rule-based generation
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