This paper addresses the task of readability assessment for the texts aimed at second language (L2) learners. One of the major challenges in this task is the lack of significantly sized level-annotated data. For the present work, we collected a dataset of CEFR-graded texts tailored for learners of English as an L2 and investigated text readability assessment for both native and L2 learners. We applied a generalization method to adapt models trained on larger native corpora to estimate text readability for learners, and explored domain adaptation and self-learning techniques to make use of the native data to improve system performance on the limited L2 data. In our experiments, the best performing model for readability on learner texts achieves an accuracy of 0.797 and P CC of 0.938.

Multilingual semantic parsing is a costeffective method that allows a single model to understand different languages. However, researchers face a great imbalance of availability of training data, with English being resource rich, and other languages having much less data. To tackle the data limitation problem, we propose using machine translation to bootstrap multilingual training data from the more abundant English data. To compensate for the data quality of machine translated training data, we utilize transfer learning from pretrained multilingual encoders to further improve the model. To evaluate our multilingual models on humanwritten sentences as opposed to machine translated ones, we introduce a new multilingual semantic parsing dataset in English, Italian and Japanese based on the Facebook Task Oriented Parsing (TOP) dataset. We show that joint multilingual training with pretrained encoders substantially outperforms our baselines on the TOP dataset and outperforms the state-of-theart model on the public NLMaps dataset. We also establish a new baseline for zero-shot learning on the TOP dataset. We find that a semantic parser trained only on English data achieves a zero-shot performance of 44.9% exact-match accuracy on Italian sentences.

In the study, high-speed counter-current chromatography was used for separation and purification of magnoflorine, spinosin, and 6‴-feruloyspinosin from Ziziphi Spinosae Semen. With n-butanol-ethyl acetate-water (2:3:5, v/v) as the optimum solvent system, about 75 mg of magnoflorine, 110 mg of spinosin, and 40 mg of 6‴-feruloyspinosin were isolated from 0.5 g of crude extract of Z. Spinosae Semen, with the purity of 95.7, 97.2, and 96.4%, respectively. The chemical structures were identified by MS and NMR spectroscopy. In addition, the antidepressant activity of the isolated components was evaluated by PC12 cells injury model and chronic unpredictable mild stress depression mouse model. The results showed that high-speed counter-current chromatography could be used to realize the one-time rapid preparation and separation of magnoflorine, spinosin, and 6‴-feruloyspinosin from Z. Spinosae Semen and compatibility of these isolated components has certain antidepressant activity.

Automating the assessment of learner summaries provides a useful tool for assessing learner reading comprehension. We present a summarization task for evaluating nonnative reading comprehension and propose three novel approaches to automatically assess the learner summaries. We evaluate our models on two datasets we created and show that our models outperform traditional approaches that rely on exact word match on this task. Our best model produces quality assessments close to professional examiners.

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