Large-Scale E nglish- J apanese Simultaneous Interpretation Corpus: Construction and Analyses with Sentence-Aligned Data (2021)

Abstract: This paper describes the construction of a new large-scale English-Japanese Simultaneous Interpretation (SI) corpus and presents the results of its analysis. A portion of the corpus contains SI data from three interpreters with different amounts of experience. Some of the SI data were manually aligned with the source speeches at the sentence level. Their latency, quality, and word order aspects were compared among the SI data themselves as well as against offline translations. The results showed that (1) interpreters with more experience controlled the latency and quality better, and (2) large latency hurt the SI quality.

## introduction simultaneous interpretation (si) is a task of translating speech from a source language into a target language in real-time. unlike consecutive translation, where the translation is done after the speaker pauses, in si the translation process starts while the speaker is still talking. with recent developments in machine translation and speech processing, various studies have been conducted aiming at automatic speech translation @xcite @xcite , including si @xcite @xcite @xcite , based on speech corpora. existing speech corpora can be classified into speech translation corpora or simultaneous interpretation corpora, as defined by @xcite . ## conclusion we described the construction of a new large-scale english↔japanese si corpus that contains si data generated by simultaneous interpreters with different amounts of experience (s-, a-, and b-ranks) from identical lectures. focusing on latency, quality, and word order, we compared the si data among interpreter ranks and against offline translations. the s-rank interpreters controlled latency and quality better than the other two ranks. we strongly believe that our new corpus will be a useful resource for further research in translation studies and for the construction of automatic si systems.