We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge bases and generate free-flow textual explanations about various health-related conditions across several dimensions.We collect and present an associated dataset, CHARDat, consisting of explanations about 52 health conditions across three clinical dimensions.We conduct extensive experiments using BART and T5 along with data augmentation, and perform automatic, human, and qualitative analyses.We show that while our models can perform decently, CHARD is very challenging with strong potential for further exploration.

While recent advances in computer vision have caused object recognition rates to spike, there is still much room for improvement. In this paper, we develop an algorithm to improve object recognition by integrating human-generated contextual information with vision algorithms. Specifically, we examine how interactive systems such as robots can utilize two types of context information--verbal descriptions of an environment and human-labeled datasets. We propose a re-ranking schema, MultiRank, for object recognition that can efficiently combine such information with the computer vision results. In our experiments, we achieve up to 9.4% and 16.6% accuracy improvements using the oracle and the detected bounding boxes, respectively, over the vision-only recognizers. We conclude that our algorithm has the ability to make a significant impact on object recognition in robotics and beyond.

Spoken language interfaces are being incorporated into various devices such as smart phones and TVs. However, dialogue systems may fail to respond correctly when users' request functionality is not supported by currently installed apps. This paper proposes a feature-enriched matrix factorization (MF) approach to model open domain intents, which allows a system to dynamically add unexplored domains according to users' requests. First we leverage the structured knowledge from Wikipedia and Freebase to automatically acquire domain-related semantics to enrich features of input utterances, and then MF is applied to model automatically acquired knowledge, published app textual descriptions and users' spoken requests in a joint fashion; this generates latent feature vectors for utterances and user intents without need of prior annotations. Experiments show that the proposed MF models incorporated with rich features significantly improve intent prediction, achieving about 34% of mean average precision (MAP) for both ASR and manual transcripts.

Unsupervised discovery of synonymous phrases is useful in a variety of tasks ranging from text mining and search engines to semantic analysis and machine translation. This paper presents an unsupervised corpus-based conditional model: Near-Synonym System (NeSS) for finding phrasal synonyms and near synonyms that requires only a large monolingual corpus. The method is based on maximizing information-theoretic combinations of shared contexts and is parallelizable for large-scale processing. An evaluation framework with crowd-sourced judgments is proposed and results are compared with alternate methods, demonstrating considerably superior results to the literature and to thesaurus look up for multi-word phrases. Moreover, the results show that the statistical scoring functions and overall scalability of the system are more important than language specific NLP tools. The method is language-independent and practically useable due to accuracy and real-time performance via parallel decomposition.

Yun-Nung Chen, William Yang Wang, Anatole Gershman, Alexander Rudnicky. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Spoken language interfaces are appearing in various smart devices (e.g. smart-phones, smart-TV, in-car navigating systems) and serve as intelligent assistants (IAs). However, most of them do not consider individual users' behavioral profiles and contexts when modeling user intents. Such behavioral patterns are user-specific and provide useful cues to improve spoken language understanding (SLU). This paper focuses on leveraging the app behavior history to improve spoken dialog systems performance. We developed a matrix factorization approach that models speech and app usage patterns to predict user intents (e.g. launching a specific app). We collected multi-turn interactions in a WoZ scenario; users were asked to reproduce the multi-app tasks that they had performed earlier on their smart-phones. By modeling latent semantics behind lexical and behavioral patterns, the proposed multi-model system achieves about 52% of turn accuracy for intent prediction on ASR transcripts.

We show that it is possible to reliably dis-criminate whether a syntactic construction is meant literally or metaphorically using lexical semantic features of the words that participate in the construction. Our model is constructed using English resources, and we obtain state-of-the-art performance relative to previous work in this language. Using a model transfer approach by piv-oting through a bilingual dictionary, we show our model can identify metaphoric expressions in other languages. We pro-vide results on three new test sets in Span-ish, Farsi, and Russian. The results sup-port the hypothesis that metaphors are conceptual, rather than lexical, in nature. 1

In general, centrality-based retrieval models treat all elements of the retrieval space equally, which may reduce their effectiveness. In the specific context of extractive summarization (or important passage retrieval), this means that these models do not take into account that information sources often contain lateral issues, which are hardly as important as the description of the main topic, or are composed by mixtures of topics. We present a new two-stage method that starts by extracting a collection of key phrases that will be used to help centrality-as-relevance retrieval model. We explore several approaches to the integration of the key phrases in the centrality model. The proposed method is evaluated using different datasets that vary in noise (noisy vs clean) and language (Portuguese vs English). Results show that the best variant achieves relative performance improvements of about 31% in clean data and 18% in noisy data.

We consider the problem of gathering data for evaluation of given hypotheses, and describe a method for analyzing tradeoffs between the expected utility and the cost of data collection.

We consider the problem of heuristic evaluation of given hypotheses based on limited observations, in situations when available data are insufficient for rigorous statistical analysis.

We present virtual training worlds that are relatively low-cost distributed collaborative learning environments suitable for corporate training. A virtual training world allows a facilitator, experts and trainees communicating and acting in the virtual environment for practicing skills during collaborative problem solving. Using these environments is beneficial to both trainees and corporations. The design of a leadership training course is discussed in details.

Challenges in multimedia analysis are calling for the sharing of research efforts, while in practice collaboration is hindered by technical and proprietary issues. The Community of Multimedia Agents project (COMMA) attempts to solve this problem by creating an open environment for developing, testing, and prototyping multimedia content analysis and annotation methods. Each method is represented as an agent (an executable module) that can communicate with the other agents based on descriptors and description schemes in the coming MPEG-7 standard. This allows multimedia-processing agents developed by different organizations to operate and collaborate with each other, regardless of their programming languages and internal architecture. The researchers can compare the performance of agents and combine them to build more powerful and robust system prototypes. It can also serve as a learning environment for researchers and students to acquire and test cutting edge multimedia analysis algorithms. Through sharing of media agents, the Community can increase efficiency of research while protecting the intellectual property of the inventors.

No abstract available.

Article Free Access Share on Opportunistic exploration of large consumer product spaces Authors: Doug Bryan Andersen Consulting, Center for Strategic, Technology Research (CSTaRlt;supgt;®lt;/supgt;), 3773 Willow Road, Northbrook, IL Andersen Consulting, Center for Strategic, Technology Research (CSTaRlt;supgt;®lt;/supgt;), 3773 Willow Road, Northbrook, ILView Profile , Anatole Gershman Andersen Consulting, Center for Strategic, Technology Research (CSTaRlt;supgt;®lt;/supgt;), 3773 Willow Road, Northbrook, IL Andersen Consulting, Center for Strategic, Technology Research (CSTaRlt;supgt;®lt;/supgt;), 3773 Willow Road, Northbrook, ILView Profile Authors Info & Claims EC '99: Proceedings of the 1st ACM conference on Electronic commerceNovember 1999 Pages 41–47https://doi.org/10.1145/336992.337003Published:01 November 1999Publication History 7citation563DownloadsMetricsTotal Citations7Total Downloads563Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Prairie is a simulation prototype or vision, demonstrating how individuals may work together in a virtual work enviroment designed for a whole enterprise. Prairie addresses various organizational and social issues exacerbated by distance and time. By using the concept of communities and by extending physical interaction cues to others across distance and time, we demonstrate possible solutions to these issues. In Prairie, people and information are organized into mission-based (organizational units), goal-based (project teams) and interest-based (special interest groups) hierarchies for ease of navigation. A worker may alternately navigate to communities by using personal links from their private virtual desktops. Each community has two areas. One area contains the information germane to a community, that is pushed or pulled depending on the nature of the information. Each community also has an area with a shared view where community members can meet or congregate. Presence in these community areas range from seeing thumbnail photos to holding a video-conference. The shared view facilitates ad hoc, informal interactions which are important for maintaining and building social networks and organizational culture. We believe the framework for Prairie is flexible, integrated, and scaleable so it can be adapted to model other organizations, communities, and processes.

This paper considers the possibilities for knowledge-based automatic text translation in the light of recent advances in artificial intelligence. It is argued that competent translation requires some reasonable depth of understanding of the source text, and, in particular, access to detailed contextual information. The following machine translation paradigm is proposed. First, the source text is analyzed and mapped into a language-free conceptual representation. Inference mechanisms then apply contextual world knowledge to augment the representation in various ways, adding information about items that were only implicit in the input text. Finally, a natural-language generator maps appropriate sections of the language-free representation into the target language. We discuss several difficult translation problems from this viewpoint with examples of English-to-Spanish and English-to-Russian translations; and illustrate possible solutions as embodied in a computer understander called SAM, which reads certain kinds of newspaper stories, then summarizes or paraphrases them in a variety of languages.