Deaf and hard-of-hearing (DHH) students face significant barriers in accessing science, technology, engineering, and mathematics (STEM) education, notably due to the scarcity of STEM resources in signed languages. To help address this, we introduce ASL STEM Wiki: a parallel corpus of 254 Wikipedia articles on STEM topics in English, interpreted into over 300 hours of American Sign Language (ASL). ASL STEM Wiki is the first continuous signing dataset focused on STEM, facilitating the development of AI resources for STEM education in ASL.We identify several use cases of ASL STEM Wiki with human-centered applications. For example, because this dataset highlights the frequent use of fingerspelling for technical concepts, which inhibits DHH students’ ability to learn,we develop models to identify fingerspelled words—which can later be used to query for appropriate ASL signs to suggest to interpreters.

This paper describes our model for COmmonsense INference in Natural Language Processing (COIN) shared task 1: Commonsense Inference in Everyday Narrations. This paper explores the use of Bidirectional Encoder Representations from Transformers(BERT) along with external relational knowledge from ConceptNet to tackle the problem of commonsense inference. The input passage, question, and answer are augmented with relational knowledge from ConceptNet. Using this technique we are able to achieve an accuracy of 73.3 % on the official test data.