Fine-tuning large language models for automated scoring is often formulated as either a regression or a classification task. However, scores assigned by human raters are on an ordinal scale. We summarize different deep-learning ordinal regression methods and compare their performances with those from regression and classification using a real dataset.

This work re-frames anomalous test response detection in standardized testing as binary image classification by transforming test response logs into fixed-size matrix representations and treating them as grayscale images. Deep learning architectures, including ResNet and Vision Transformer, are evaluated. Experiments demonstrate that the approach is feasible and achieves high recall.

Selectional Preference (SP) captures the tendency of a word to semantically select other words to be in direct syntactic relation with it, and thus informs us about syntactic word configurations that are meaningful. Therefore SP is a valuable resource for Natural Language Processing (NLP) systems and for semanticists. Learning SP has generally been seen as a supervised task, because it requires a parsed corpus as a source of syntactically related word pairs. In this paper we show that simple distributional analysis can learn a good amount of SP without the need for an annotated corpus. We extend the general word embedding technique with directional word context windows giving word representations that better capture syntagmatic relations. We test on the SP-10K dataset and demonstrate that syntagmatic embeddings outperform the paradigmatic embeddings. We also evaluate supervised version of these embeddings and show that unsupervised syntagmatic embeddings can be as good as supervised embeddings. We also make available the source code of our implementation.