Paraphrasing and translation
Paraphrasing and translation have previously been treated as unconnected natural language processing tasks. We show the two are intimately related. The major contributions of this thesis are as follows: We define a novel technique for automatically generating paraphrases using bilingual parallel corpora, which are more commonly used as training data statistical models of translation. We show that paraphrases can be used to improve the quality of statistical machine translation by addressing the problem of coverage and introducing a degree of generalisation into the models. We explore the topic of automatic evaluation of translation quality, and show that the current standard evaluation methodology cannot be guaranteed to correlate with human judgements of translation quality. Whereas previous data-driven approaches to paraphrasing were dependent upon either data sources which were uncommon such as multiple translation of the same source text, or language specific resources such as parsers, our approach is able to harness more widely parallel corpora and can be applied to any language which has a parallel corpus. Being a language independent and probabilistic approach allows our method to be easily integrated
Callison-Burch, Chris
Paraphrasing and translation. Paraphrasing and translation have previously been treated as unconnected natural language processing tasks. We show the two are intimately related. The major contributions of this thesis are as follows: We define a novel technique for automatically generating paraphrases using bilingual parallel corpora, which are more commonly used as training data statistical models of translation. We show that paraphrases can be used to improve the quality of statistical machine translation by addressing the problem of coverage and introducing a degree of generalisation into the models. We explore the topic of automatic evaluation of translation quality, and show that the current standard evaluation methodology cannot be guaranteed to correlate with human judgements of translation quality. Whereas previous data-driven approaches to paraphrasing were dependent upon either data sources which were uncommon such as multiple translation of the same source text, or language specific resources such as parsers, our approach is able to harness more widely parallel corpora and can be applied to any language which has a parallel corpus. Being a language independent and probabilistic approach allows our method to be easily integrated
translation translation previously treat unconnected natural_language_processing task show intimately relate major contribution follow define novel technique automatically generate paraphrase use bilingual parallel_corpus commonly_use training_data statistical_model translation show paraphrase use improve_quality statistical_machine_translation address_problem coverage introduce degree generalisation model explore topic automatic evaluation translation_quality show current standard evaluation guarantee correlate human judgement translation_quality whereas_previous data_driven approach paraphrase dependent_upon either data_source uncommon multiple translation source_text language_specific resource parser able harness widely parallel_corpus apply language parallel_corpus language independent probabilistic allows method easily integrate