Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning. This paper presents the new state-of-theart on WSC, achieving an accuracy of 71.1%. We demonstrate that the leading performance benefits from jointly modelling sentence structures, utilizing knowledge learned from cutting-edge pretraining models, and performing fine-tuning. We conduct detailed analyses, showing that fine-tuning is critical for achieving the performance, but it helps more on the simpler associative problems. Modelling sentence dependency structures, however, consistently helps on the harder non-associative subset of WSC. Analysis also shows that larger fine-tuning datasets yield better performances, suggesting the potential benefit of future work on annotating more Winograd schema sentences.

In online education systems, for offering proactive services to students (e.g., personalized exercise recommendation), a crucial demand is to predict student performance (e.g., scores) on future exercising activities. Existing prediction methods mainly exploit the historical exercising records of students, where each exercise is usually represented as the manually labeled knowledge concepts, and the richer information contained in the text description of exercises is still underexplored. In this paper, we propose a novel Exercise-Enhanced Recurrent Neural Network (EERNN) framework for student performance prediction by taking full advantage of both student exercising records and the text of each exercise. Specifically, for modeling the student exercising process, we first design a bidirectional LSTM to learn each exercise representation from its text description without any expertise and information loss. Then, we propose a new LSTM architecture to trace student states (i.e., knowledge states) in their sequential exercising process with the combination of exercise representations. For making final predictions, we design two strategies under EERNN, i.e., EERNNM with Markov property and EERNNA with Attention mechanism. Extensive experiments on large-scale real-world data clearly demonstrate the effectiveness of EERNN framework. Moreover, by incorporating the exercise correlations, EERNN can well deal with the cold start problems from both student and exercise perspectives.

Standard tests aim to evaluate the performance of examinees using different tests with consistent difficulties. Thus, a critical demand is to predict the difficulty of each test question before the test is conducted. Existing studies are usually based on the judgments of education experts (e.g., teachers), which may be subjective and labor intensive. In this paper, we propose a novel Test-aware Attention-based Convolutional Neural Network (TACNN) framework to automatically solve this Question Difficulty Prediction (QDP) task for READING problems (a typical problem style in English tests) in standard tests. Specifically, given the abundant historical test logs and text materials of questions, we first design a CNN-based architecture to extract sentence representations for the questions. Then, we utilize an attention strategy to qualify the difficulty contribution of each sentence to questions. Considering the incomparability of question difficulties in different tests, we propose a test-dependent pairwise strategy for training TACNN and generating the difficulty prediction value. Extensive experiments on a real-world dataset not only show the effectiveness of TACNN, but also give interpretable insights to track the attention information for questions.