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Found 18 papers, 4 papers with code
Tell Me Who Your Students Are: GPT Can Generate Valid Multiple-Choice Questions When Students' (Mis)Understanding Is Hinted
To evaluate the validity of the generated MCQs, Item Response Theory (IRT) was applied to compare item characteristics between MCQs generated by AnaQuest, a baseline ChatGPT prompt, and human-crafted items.
Automatic Feedback Generation for Short Answer Questions using Answer Diagnostic Graphs
We propose an "answer diagnosis graph," integrating the text's logical structure with feedback templates.
Reducing the Cost: Cross-Prompt Pre-Finetuning for Short Answer Scoring
Specifically, given that scoring rubrics and reference answers differ for each prompt, we utilize key phrases, or representative expressions that the answer should contain to increase scores, and train a SAS model to learn the relationship between key phrases and answers using already annotated prompts (i. e., cross-prompts).
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese
Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages.
Japanese-English Sentence Translation Exercises Dataset for Automatic Grading
Using this dataset, we demonstrate the performance of baselines including finetuned BERT and GPT models with few-shot in-context learning.
Balancing Cost and Quality: An Exploration of Human-in-the-loop Frameworks for Automated Short Answer Scoring
Towards guaranteeing high-quality predictions, we present the first study of exploring the use of human-in-the-loop framework for minimizing the grading cost while guaranteeing the grading quality by allowing a SAS model to share the grading task with a human grader.
Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution
Masked language models (MLMs) have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR).
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution
This study addresses two underexplored issues on CDA, that is, how to reduce the computational cost of data augmentation and how to ensure the quality of the generated data.
Preventing Critical Scoring Errors in Short Answer Scoring with Confidence Estimation
We introduce a new task formulation of SAS that matches the actual usage.
Distance-Free Modeling of Multi-Predicate Interactions in End-to-End Japanese Predicate-Argument Structure Analysis
Capturing interactions among multiple predicate-argument structures (PASs) is a crucial issue in the task of analyzing PAS in Japanese.
A Melody-Conditioned Lyrics Language Model
This paper presents a novel, data-driven language model that produces entire lyrics for a given input melody.
Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis
The research trend in Japanese predicate-argument structure (PAS) analysis is shifting from pointwise prediction models with local features to global models designed to search for globally optimal solutions.
Modeling Discourse Segments in Lyrics Using Repeated Patterns
This study proposes a computational model of the discourse segments in lyrics to understand and to model the structure of lyrics.
Modeling Context-sensitive Selectional Preference with Distributed Representations
This paper proposes a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP).
Building Japanese Predicate-argument Structure Corpus using Lexical Conceptual Structure
In this paper, we report our framework for creating the corpus and the current status of creating an LCS dictionary for Japanese predicates.
