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It remains a question that how simultaneous interpretation (SI) data affectssimultaneous machine translation (SiMT). Research has been limited due to thelack of a large-scale training corpus. In this work, we aim to fill in the gapby introducing NAIST-SIC-Aligned, which is an automatically-aligned parallelEnglish-Japanese SI dataset. Starting with a non-aligned corpus NAIST-SIC, wepropose a two-stage alignment approach to make the corpus parallel and thussuitable for model training. The first stage is coarse alignment where weperform a many-to-many mapping between source and target sentences, and thesecond stage is fine-grained alignment where we perform intra- andinter-sentence filtering to improve the quality of aligned pairs. To ensure thequality of the corpus, each step has been validated either quantitatively orqualitatively. This is the first open-sourced large-scale parallel SI datasetin the literature. We also manually curated a small test set for evaluationpurposes. We hope our work advances research on SI corpora construction andSiMT. Please find our data at \url{https://github.com/mingzi151/AHC-SI}.

NAIST-SIC-Aligned: Automatically-Aligned English-Japanese Simultaneous Interpretation Corpus