  It remains a question that how simultaneous interpretation (SI) data affects
simultaneous machine translation (SiMT). Research has been limited due to the
lack of a large-scale training corpus. In this work, we aim to fill in the gap
by introducing NAIST-SIC-Aligned, which is an automatically-aligned parallel
English-Japanese SI dataset. Starting with a non-aligned corpus NAIST-SIC, we
propose a two-stage alignment approach to make the corpus parallel and thus
suitable for model training. The first stage is coarse alignment where we
perform a many-to-many mapping between source and target sentences, and the
second stage is fine-grained alignment where we perform intra- and
inter-sentence filtering to improve the quality of aligned pairs. To ensure the
quality of the corpus, each step has been validated either quantitatively or
qualitatively. This is the first open-sourced large-scale parallel SI dataset
in the literature. We also manually curated a small test set for evaluation
purposes. Our results show that models trained with SI data lead to significant
improvement in translation quality and latency over baselines. We hope our work
advances research on SI corpora construction and SiMT. Our data can be found at
https://github.com/mingzi151/AHC-SI.
