This paper proposes a hybrid machine-translation system that combines neural machine translation with well-developed rule-based machine translation to utilize the stability of the latter to compensate for the inadequacy of neural machine translation in rare-resource domains. A classifier is introduced to predict which translation from the two systems is more reliable. We explore a set of features that reflect the reliability of translation and its process, and training data is automatically expanded with a small, human-labeled dataset to solve the insufficient-data problem. A series of experiments shows that the hybrid system’s translation accuracy is improved, especially in out-of-domain translations, and classification accuracy is greatly improved when using the proposed features and the automatically constructed training set. A comparison between feature- and text-based classification is also performed, and the results show that the feature-based model achieves better classification accuracy, even when compared to neural network text classifiers.

A major challenge in education is to provide students with a personalized learning experience. This study aims to address this by developing a dialogue-based intelligent tutoring system (ITS) that imitates human expert tutors. The ITS asks questions, assesses student answers, provides hints, and even chats to encourage student engagement. We constructed the Dialogue-based Reading Comprehension Tutoring (DIRECT) dataset to simulate real-world pedagogical scenarios with the assessment labels and key sentences to support tutoring. The DIRECT dataset is based on RACE, which is a large-scale English reading comprehension dataset. In addition, we propose a neural pipeline approach to model the tutoring tasks and conduct a comprehensive analysis on the results, including a human evaluation. The results show that our model performs well in generating questions, assessing answers, and chatting, showing high potential although some challenges remain. The proposed model provides a good basis for further development of dialogue-based ITSs. The dataset will be publicly available after the publication.

In this paper, an iterative algorithm for solving the strong vector equilibrium problem with variable domination structure (VSVEP) is considered. First, an auxiliary problem for the VSVEP is introduced and the relationships between the auxiliary problem and VSVEP are discussed. Then, using the auxiliary principle technique, a projection iterative algorithm to compute the approximate solutions of the VSVEP is proposed and analysed. Furthermore, convergence of the iterative sequences generated by this algorithm is investigated under suitable conditions of continuity and convexity. These results extend and improve some recent works in this field.

Automated Essay Scoring (AES) plays a crucial role in educational assessment, yet building reliable systems often requires large amounts of prompt- and trait-specific labeled data. As a result, many studies rely heavily on a single public dataset, ASAP++, which limits their generalizability. In this paper, we explore diverse zero-shot AES approaches that are both prompt-agnostic and multi-trait, thereby reducing reliance on labeled data. Our study examines large language models (LLMs), BERT-like fine-tuned models, and simple feature-based methods. Furthermore, we propose two novel zero-shot frameworks: CAnUSe (Comparative Assessment and subsequent Uncertainty-aware Self-training) and EASY (Essay Assessment with Simple Yardsticks). CAnUSe combines LLM-based pairwise comparisons with uncertainty-aware self-training using a BERT-like single-essay scoring model, while EASY employs only three intuitive features-essay length, vocabulary diversity, and grammar quality. Evaluation on three benchmark datasets-ASAP++, Ellipse, and TOEFL11-shows that our frameworks achieve state-of-the-art performance, demonstrating strong generalizability across datasets. Remarkably, despite operating in a zero-shot setting, our models approach the performance of fully supervised models trained with approximately 10K labeled samples. Further analysis provides practical insights for building real-world AES systems.