Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment

Human communication often involves information gaps between the interlocutors. For example, in an educational dialogue, a student often provides an answer that is incomplete, and there is a gap between this answer and the perfect one expected by the teacher. Successful dialogue then hinges on the teacher asking about this gap in an effective manner, thus creating a rich and interactive educational experience. We focus on the problem of generating such gap-focused questions (GFQs) automatically. We define the task, highlight key desired aspects of a good GFQ, and propose a model that satisfies these. Finally, we provide an evaluation by human annotators of our generated questions compared against human generated ones, demonstrating competitive performance

Interactive Tool for Researching Large Unstructured Document Collections

Reviewing large document collections is an activity that arises commonly in certain professional contexts such as investigative journalism. Such document collections can arise in many use contexts such as investigative journalism; academic research; litigation, arbitration or other legal context; audit; research using document archives; etc. The collections may include a large number of documents, including scanned images of documents or handwritten documents, and are often devoid of structure or organization. This makes it difficult to sift through such collections and identify important pieces of information. This disclosure describes a tool that enables easier access to such collections and features that support review and research based on such document collections. Automated techniques such as optical character recognition, entity recognition, indexing, etc. are utilized to process the document collection to index the documents and to generate timelines, connection graphs, or other views on the collection. A user interface is provided that enables users to search the collection, view event timelines, make annotations, take notes, and collaborate with others. The described techniques facilitate sensemaking and can help surface latent insight

Reading Comprehension Assessment Using LLM-based Chatbot

Users who are learning to read or learning about a topic by viewing content on a device can benefit from conversational activities, such as question-answer turns for the viewed content. This disclosure describes techniques to perform natural language assessments of content that is being consumed on a user device. A chatbot is implemented using suitable technology, such as a large language model. With user permission, the model is used to generate questions that evaluate the user’s understanding of the content viewed. User provided answers are evaluated and suitable responses are provided to the user. The techniques enable automated assessment and feedback. The described features for assessment via chatbot (or virtual assistant) can be built into any application. Assessment is performed on-device and in a confidential manner

The Regional climate

Regional climate is typically affected by atmospheric, oceanic and land processes at all scales (Pielke 2001a). However, this chapter focuses on the issue "Does the horizontal heterogeneity of the soil-vegetation-atmosphere system affect the regional climate?"12 page(s