This paper presents the development of an AI-based language learning platform
Revita. It is a freely available intelligent online tutor, developed to support
learners of multiple languages, from low-intermediate to advanced levels. It
has been in pilot use by hundreds of students at several universities, whose
feedback and needs are shaping the development. One of the main emerging
features of Revita is the introduction of a system of linguistic constructs as
the representation of domain knowledge. The system of constructs is developed
in close collaboration with experts in language teaching. Constructs define the
types of exercises, the content of the feedback, and enable the detailed
modeling and evaluation of learning progress.

本文介绍了一种基于人工智能的语言学习平台 Revita，该平台为学习者提供智能的在线辅导，采用了一套语言构造系统作为领域知识的表达方式，此系统由语言教学专家与开发团队紧密合作开发，可定义练习类型、反馈内容并评估学习进展。

智能语言辅导系统中语言结构作为领域模型的表示

Linguistic Constructs as the Representation of the Domain Model in an Intelligent Language Tutoring System

We propose a novel method for exploiting the semantic structure of text to
answer multiple-choice questions. The approach is especially suitable for
domains that require reasoning over a diverse set of linguistic constructs but
have limited training data. To address these challenges, we present the first
system, to the best of our knowledge, that reasons over a wide range of
semantic abstractions of the text, which are derived using off-the-shelf,
general-purpose, pre-trained natural language modules such as semantic role
labelers, coreference resolvers, and dependency parsers. Representing multiple
abstractions as a family of graphs, we translate question answering (QA) into a
search for an optimal subgraph that satisfies certain global and local
properties. This formulation generalizes several prior structured QA systems.
Our system, SEMANTICILP, demonstrates strong performance on two domains
simultaneously. In particular, on a collection of challenging science QA
datasets, it outperforms various state-of-the-art approaches, including neural
models, broad coverage information retrieval, and specialized techniques using
structured knowledge bases, by 2%-6%.

提出了一种利用文本的语义结构来回答多项选择题的新方法，将 QA 转化为一种满足某些全局和局部特性的最优子图的搜索问题，在科学 QA 数据集上表现良好。