  Inspired by early research on exploring naturally annotated data for Chinese
word segmentation (CWS), and also by recent research on integration of speech
and text processing, this work for the first time proposes to mine word
boundaries from parallel speech/text data. First we collect parallel
speech/text data from two Internet sources that are related with CWS data used
in our experiments. Then, we obtain character-level alignments and design
simple heuristic rules for determining word boundaries according to pause
duration between adjacent characters. Finally, we present an effective
complete-then-train strategy that can better utilize extra naturally annotated
data for model training. Experiments demonstrate our approach can significantly
boost CWS performance in both cross-domain and low-resource scenarios.
