The NLP Group at Penn State

  Congratulations! A paper by Zhaohui Li, Yajur Tomar, and Rebecca J. Passonneau,  "A Semantic Feature-Wise Transformation Relation Network for Automatic Short Answer Grading", has been accepted to the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) Main conference.  This paper is about the Automatic short answer grading (ASAG), the task of assessing students’ short natural language responses to objective questions. In this paper, we propose a Semantic Feature-wise transformation Relation Network (SFRN) that exploits the multiple components of ASAG datasets more effectively. As shown in Figure 2, a neural network is applied to capture relational knowledge among the questions (Q), reference answers or rubrics (R), and labeled student answers (A). A relation network learns vector representations for the elements of QRA triples, then combines the learned representations using learned semantic feature-wise transformations. In addition, we apply tr

Maryam successfully defended her thesis defense on 14th June 2021. The online copy of the thesis can be found online using the link: https://etda.libraries.psu.edu/catalog/22618muz50  Title:  AN AGENT LEARNING DIALOGUE POLICIES FOR SENSING PERCEIVING AND LEARNING THROUGH MULTI-MODAL COMMUNICATION Abstract: Language communication is an important part of human life and a natural and intuitive way of learning new things. It is easy to imagine intelligent agents that can learn through communication to for example, help us in rescue scenarios, surgery, or even agriculture. As natural as learning through language is to humans, developing such agents has numerous challenges: Language is ambiguous, and humans convey their intentions in different ways using different words. Tasks have different learning goals and some are more complex than others. Additionally, humans differ in their communicative skills, particularly, in how much information they share or know. Thus, the agent must b