Second language learners studying languages with a diverse set of prepositions often find preposition usage difficult to master, which can manifest in second language writing as preposition errors that appear to result from transfer from a native language, or interlingual errors. We envision a digital writing assistant for language learners and teachers that can provide targeted feedback on these errors. To address these errors, we turn to the task of preposition error detection, which remains an open problem despite the many methods that have been proposed. In this paper, we explore various classifiers, with and without neural network-based features, and fine-tuned BERT models for detecting preposition errors between verbs and their noun arguments.

Visualization, while an effective tool for identifying patterns and insights, requires expert knowledge due to challenges faced when translating user queries to visual encodings. Research has shown that using a natural language interface (NLI) is effective for these challenges because the user can simply talk to a computer capable of producing the graphs directly. In this paper, we discuss our intelligent assistant which processes speech and hand pointing gestures while also dealing with any number of visualizations on a large screen display. Evaluation of the system shows that it is capable of quickly producing visualizations. It also particularly effective at responding to less ambiguous queries, while in certain cases can handle ambiguous or complex queries.

In this position paper, we describe the RoboHelper project, its findings and our vision for its future. The long-term goal of RoboHelper is to develop assistive robots for the elderly. The main thesis of our work is that such robots must crucially be able to participate in multimodal dialogues. Contributions of our work to date include the ELDERLY-AT-HOME corpus that we collected and annotated. It consists of 20 task-oriented human-human dialogues between a helper and an elderly person in a fully functional apartment. The unique feature of the corpus is that in addition to video and audio, it includes recordings of physical interaction. Based on this data, we have demonstrated the crucial role that Haptic-Ostensive (H-O) actions play in interpreting language and uncovering a person's intentions. H-O actions manipulate objects, but they also often perform a referring function. Our models were derived on the basis of manually annotated categories. Additional experiments show that we can identify H-O actions using the physical interaction data measured through an unobtrusive sensory glove developed as part of the project. In future work, we will derive models for the robot to decide what to do next (as opposed to interpreting what the interlocutor did); explore other types of physical interactions; and refine preliminary implementations of our models on the Nao robotic platform.

The Association for the Advancement of Artificial Intelligence held its 1996 Spring Symposia Series on March 27 to 29 at Stanford University. This article contains summaries of the eight symposia that were conducted: (1) Acquisition, Learning, and Demonstration: Automating Tasks for Users; (2) Adaptation, Coevolution, and Learning in Multiagent Systems; (3) Artificial Intelligence in Medicine: Applications of Current Technologies; (4) Cognitive and Computational Models of Spatial Representation; (5) Computational Implicature: Computational Approaches to Interpreting and Generating Conversational Implicature; (6) Computational Issues in Learning Models of Dynamic Systems; (7) Machine Learning in Information Access; and (8) Planning with Incomplete Information for Robot Problems.

The Association for the Advancement of Artificial Intelligence held its 1996 Spring Symposia Series on March 27 to 29 at Stanford University. This article contains summaries of the eight symposia that were conducted: (1) Acquisition, Learning, and Demonstration: Automating Tasks for Users; (2) Adaptation, Coevolution, and Learning in Multiagent Systems; (3) Artificial Intelligence in Medicine: Applications of Current Technologies; (4) Cognitive and Computational Models of Spatial Representation; (5) Computational Implicature: Computational Approaches to Interpreting and Generating Conversational Implicature; (6) Computational Issues in Learning Models of Dynamic Systems; (7) Machine Learning in Information Access; and (8) Planning with Incomplete Information for Robot Problems.