Toward a Broad-coverage Bilingual Corpus for Speech Translation of Travel Conversations in the Real World
By T. Takezawa, Eiichiro Sumita, F. Sugaya, H. Yamamoto, Seiichi Yamamoto. International Conference on Language Resources and Evaluation, 2002
299 citations recorded by the source corpus.
Abstract At ATR Spoken Language Translation Research Laboratories, we are building a broad-coverage bilingual corpus to study corpus-based speech translation technologies for the real world. There are three important points to consider in designing and constructing a corpus for future speech translation research. The first is to have a variety of speech samples, with a wide range of pronunciations and speakers. The second is to have data for a variety of situations. The third is to have a variety of expressions. This paper reports our trials and discusses the methodology. First, we introduce a bilingual travel conversation (TC) corpus of spoken languages and a broad-coverage bilingual basic expression (BE) corpus. TC and BE are designed to be complementary. TC is a collection of transcriptions of bilingual spoken dialogues, while BE is a collection of Japanese sentences and their English translations. Whereas TC covers a small domain, BE covers a wide variety of domains. We compare the characteristics of vocabulary and expressions between these two corpora and suggest that we need a much greater variety of expressions. One promising approach might be to collect paraphrases representing various different expressions generated by many people for similar concepts.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/79794914adfbe844151b01bd0216c275cc33de4f
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In Artificial Intelligence
- Marco Turchi
- Marcello Federico
- Mauro Cettolo
- Satoshi Nakamura
- Roldano Cattoni
- Eiichiro Sumita
- Luisa Bentivogli
- Sophie Rosset
Citations in the corpus
- DiaBLa: a corpus of bilingual spontaneous written dialogues for machine translation (2019, background): “For example BTEC (Basic Travel Expression Corpus) and SLDB (Spoken Language DataBase) are both focused on the travel domain and represent a particular style of conversation structured around a task.”
- The IWSLT 2018 Evaluation Campaign (2018, methodology): “The evaluation campaign built on one of the outcomes of the C-STAR (Consortium for Speech Translation Advanced Research) project, namely the BTEC (Basic Travel Expression Corpus) multilingual spoken language corpus [2], which initially served as a primary source of evaluation.”
- The IWSLT 2016 Evaluation Campaign (2016, methodology): “The evaluation campaign built on one of the outcomes of the C-STAR (Consortium for Speech Translation Advanced Research) project, namely the BTEC (Basic Travel Expression Corpus) multilingual spoken language corpus [2], which initially served as a primary source of evaluation.”
- The ATR Multilingual Speech-to-Speech Translation System (2006, methodology): “ATR has been constructing two different types of corpora in the travel domain: 1) a large-scale multilingual collection of basic sentences that covers many domains [ 37 ], and 2) a small-scale bilingual collection of spoken sentences that reflects the characteristics of the spoken dialogs [38].”
- Recurrent Neural Networks for Word Alignment Model (2014, methodology): “…the alignment performance of the proposed models with two tasks: Japanese-English word alignment with the Basic Travel Expression Corpus ( BT EC ) (Takezawa et al., 2002) and French-English word alignment with the Hansard dataset ( Hansards ) from the 2003 NAACL shared task (Mihalcea and…”