  Spoken language proficiency is critically important for children's growth and
personal development. Due to the limited and imbalanced educational resources
in China, elementary students barely have chances to improve their oral
language skills in classes. Verbal fluency tasks (VFTs) were invented to let
the students practice their spoken language proficiency after school. VFTs are
simple but concrete math related questions that ask students to not only report
answers but speak out the entire thinking process. In spite of the great
success of VFTs, they bring a heavy grading burden to elementary teachers. To
alleviate this problem, we develop Dolphin, a spoken language proficiency
assessment system for Chinese elementary education. Dolphin is able to
automatically evaluate both phonological fluency and semantic relevance of
students' VFT answers. We conduct a wide range of offline and online
experiments to demonstrate the effectiveness of Dolphin. In our offline
experiments, we show that Dolphin improves both phonological fluency and
semantic relevance evaluation performance when compared to state-of-the-art
baselines on real-world educational data sets. In our online A/B experiments,
we test Dolphin with 183 teachers from 2 major cities (Hangzhou and Xi'an) in
China for 10 weeks and the results show that VFT assignments grading coverage
is improved by 22\%.
