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  1. Understanding spatial expressions and using them appropriately is necessary for seamless and natural human-machine interaction. However, capturing the semantics and appropriate usage of spatial prepositions is notoriously difficult, because of their vagueness and polysemy. Although modern data-driven approaches are good at capturing statistical regularities in the usage, they usually require substantial sample sizes, often do not generalize well to unseen instances and, most importantly, their structure is essentially opaque to analysis, which makes diagnosing problems and understanding their reasoning process difficult. In this work, we discuss our attempt at modeling spatial senses of prepositions in English using a combination ofmore »rule-based and statistical learning approaches. Each preposition model is implemented as a tree where each node computes certain intuitive relations associated with the preposition, with the root computing the final value of the prepositional relation itself. The models operate on a set of artificial 3D “room world” environments, designed in Blender, taking the scene itself as an input. We also discuss our annotation framework used to collect human judgments employed in the model training. Both our factored models and black-box baseline models perform quite well, but the factored models will enable reasoned explanations of spatial relation judgements.« less
    Free, publicly-accessible full text available August 5, 2022
  2. “Episodic Logic: Unscoped Logical Form” (EL-ULF) is a semantic representation capturing predicate-argument structure as well as more challenging aspects of language within the Episodic Logic formalism. We present the first learned approach for parsing sentences into ULFs, using a growing set of annotated examples. The results provide a strong baseline for future improvement. Our method learns a sequence-to-sequence model for predicting the transition action sequence within a modified cache transition system. We evaluate the efficacy of type grammar-based constraints, a word-to-symbol lexicon, and transition system state features in this task. Our system is availableat ulf-transition-parser. We also present themore »first official annotated ULF dataset at gkim21/ulf/resources/.« less
  3. Objective: Communication difficulties negatively impact relationship quality and are associated with social isolation and loneliness in later life. There is a need for accessible communication interventions offered outside specialty mental health settings. Design: Pilot randomized controlled trial. Setting: Assessments in the laboratory and intervention completed in-home. Participants: Twenty adults age 60 and older from the community and a geriatric psychiatry clinic. Intervention: A web-based communication coach that provides automated feedback on eye contact, facial expressivity, speaking volume, and negative content (Aging and Engaging Program, AEP), delivered with minimal assistance in the home (eight brief sessions over 4–6 weeks) or controlmore »(education and videos on communication). Measurements: System Usability Scale and Social Skills Performance Assessment, an observer-rated assessment of social communication elicited through standardized role-plays. Results" Ninety percent of participants completed all AEP sessions and the System Usability Scale score of 68 was above the cut-off for acceptable usability. Participants randomized to AEP demonstrated statistically and clinically significant improvement in eye contact and facial expressivity. Conclusion: The AEP is acceptable and feasible for older adults with communication difficulties to complete at home and may improve eye contact and facial expressivity, warranting a larger RCT to confirm efficacy and explore potential applications to other populations, including individuals with autism and social anxiety.« less
    Free, publicly-accessible full text available August 1, 2022
  4. In this paper, we describe the iterative participatory design of SOPHIE, an online virtual patient for feedback-based practice of sensitive patient-physician conversations, and discuss an initial qualitative evaluation of the system by professional end users. The design of SOPHIE was motivated from a computational linguistic analysis of the transcripts of 383 patient-physician conversations from an essential office visit of late stage cancer patients with their oncologists. We developed methods for the automatic detection of two behavioral paradigms, lecturing and positive language usage patterns (sentiment trajectory of conversation), that are shown to be significantly associated with patient prognosis understanding. These automatedmore »metrics associated with effective communication were incorporated into SOPHIE, and a pilot user study identified that SOPHIE was favorably reviewed by a user group of practicing physicians.« less
  5. We present a system for learning generalized, stereotypical patterns of events—or “schemas”—from natural language stories, and applying them to make predictions about other stories. Our schemas are represented with Episodic Logic, a logical form that closely mirrors natural language. By beginning with a “head start” set of protoschemas—schemas that a 1- or 2-year-old child would likely know—we can obtain useful, general world knowledge with very few story examples—often only one or two. Learned schemas can be combined into more complex, composite schemas, and used to make predictions in other stories where only partial information is available.
  6. We present a method of making natural logic inferences from Unscoped Logical Form of Episodic Logic. We establish a correspondence between inference rules of scope-resolved Episodic Logic and the natural logic treatment by Sánchez Valencia (1991a), and hence demonstrate the ability to handle foundational natural logic inferences from prior literature as well as more general nested monotonicity inferences.
  7. A physical blocks world, despite its relative simplicity, requires (in fully interactive form) a rich set of functional capabilities, ranging from vision to natural language understanding. In this work we tackle spatial question answering in a holistic way, using a vision system, speech input and output mediated by an animated avatar, a dialogue system that robustly interprets spatial queries, and a constraint solver that derives answers based on 3-D spatial modeling. The contributions of this work include a semantic parser that maps spatial questions into logical forms consistent with a general approach to meaning representation, a dialogue manager based onmore »a schema representation, and a constraint solver for spatial questions that provides answers in agreement with human perception. These and other components are integrated into a multi-modal human-computer interaction pipeline.« less