Spoken Language Assessment (SLA) estimates a learner's oral proficiency from spontaneous speech. The growing population of L2 English speakers has intensified the demand for reliable SLA, a critical component of Computer Assisted Language Learning (CALL). Existing efforts often rely on cascaded pipelines, which are prone to error propagation, or end-to-end models that often operate on a short audio window, which might miss discourse-level evidence. This paper introduces a novel multimodal foundation model approach that performs session-level evaluation in a single pass. Our approach couples multi-target learning with a frozen, Whisper ASR model-based speech prior for acoustic-aware calibration, allowing for jointly learning holistic and trait-level objectives of SLA without resorting to handcrafted features. By coherently processing the entire response session of an L2 speaker, the model excels at predicting holistic oral proficiency. Experiments conducted on the Speak & Improve benchmark demonstrate that our proposed approach outperforms the previous state-of-the-art cascaded system and exhibits robust cross-part generalization, producing a compact deployable grader that is tailored for CALL applications.

Large-scale knowledge graphs (KGs) provide structured representations of human knowledge. However, as it is impossible to contain all knowledge, KGs are usually incomplete. Reasoning based on existing facts paves a way to discover missing facts. In this paper, we study the problem of learning logic rules for reasoning on knowledge graphs for completing missing factual triplets. Learning logic rules equips a model with strong interpretability as well as the ability to generalize to similar tasks. We propose a model called MPLR that improves the existing models to fully use training data and multi-target scenarios are considered. In addition, considering the deficiency in evaluating the performance of models and the quality of mined rules, we further propose two novel indicators to help with the problem. Experimental results empirically demonstrate that our MPLR model outperforms state-of-the-art methods on five benchmark datasets. The results also prove the effectiveness of the indicators.

One-step abductive multi-target learning (OSAMTL) [1] was proposed to alleviate the situation where it is often difficult or even impossible for experts to manually achieve the accurate ground-truth labels, which leads to labels with complex noisy for a specific learning task. With a H. pylori segmentation task of medical histopathology whole slide images [1,2], OSAMTL has been shown to possess significant potentials in handling complex noisy labels, using logical rationality evaluations based on logical assessment formula (LAF) [1,3]. However, OSAMTL is not suitable for the situation of learning with diverse noisy label samples. In this paper, we aim to address this issue. Firstly, we give definition of diverse noisy label samples (DNLS). Secondly, based on the given definition of DNLS, we propose one-step abductive multi-target learning with DNLS (OSAMTL-DNLS). Finally, we provide analyses of OSAMTL-DNLS compared with the original OSAMTL.