In the last few years, a large number of automatic evaluation metrics have been proposed for
evaluating Natural Language Generation (NLG) systems. The rapid development and …

Currently, there is little agreement as to how Natural Language Generation (NLG) systems
should be evaluated, with a particularly high degree of variation in the way that human …

Human evaluation is indispensable and inevitable for assessing the quality of texts
generated by machine learning models or written by humans. However, human evaluation is …

Human evaluations are typically considered the gold standard in natural language
generation, but as models' fluency improves, how well can evaluators detect and judge …

The paper surveys evaluation methods of natural language generation (NLG) systems that
have been developed in the last few years. We group NLG evaluation methods into three …

Question answering (QA) is a fundamental means to facilitate assessment and training of
narrative comprehension skills for both machines and young children, yet there is scarcity of …

Generating short answer questions is a popular form of learnersourcing with benefits for
both the students' higher-order thinking and the instructors' collection of assessment items …

Where have we been, and where are we going? It is easier to talk about the past than the
future. These days, benchmarks evolve more bottom up (such as papers with code). There …

Multiple-choice questions with item-writing flaws can negatively impact student learning and
skew analytics. These flaws are often present in student-generated questions, making it …

We introduce a counseling dialogue system that seeks to assist counselors while they are
learning and refining their counseling skills. The system generates counselors' reflections …