Inclusive team participation is one of the most important factors that aids effective collaboration and pair programming. In this paper, we investigated the ability of linguistic features and a transformer-based language model to detect exclusive and inclusive language. The task of detecting exclusive language was approached as a text classification problem. We created a research community resource consisting of a dataset of 40,490 labeled utterances obtained from three programming assignments involving 34 students pair programming in a remote environment. This research involves the first successful automated detection of exclusive language during pair programming. Additionally, this is the first work to perform a computational linguistic analysis on the verbal interaction common in the context of inclusive and exclusive language during pair programming.

A fundamental management responsibility is securing information systems. Almost all applications that deal with safety, privacy, or defense include some form of access control. There are a plethora of access control models in the information security realm such as role-based access control and attribute-based access control. However, the initial development of access control policies (ACPs) can be very challenging. Most organizations have high-level requirement specifications that include a set of ACPs, which describe allowable operations of the system. It is time consuming and error-prone to manually sift through these documents and extract ACPs. In this paper, we propose a new framework towards extracting ACPs from unrestricted natural language documents using semantic role labeling (SRL). We were able to correctly identify ACP elements with an average F1 score of 75 percent, which bested the previous work by 15 percent. Furthermore, as SRL tools are often trained on publicly available corpora such as Wall Street Journal, we investigated the idea of improving SRL performance using domain-related knowledge. We utilized domain adaptation and semi-supervised learning techniques and were able to improve the SRL performance by 2 percent using only a small amount of access control data.

The next generation of educational applications need to significantly improve the way feedback is offered to both teachers and students. Simply determining coarse-grained entailment relations between the teacher's reference answer as a whole and a student response will not be sufficient. A finer-grained analysis is needed to determine which aspects of the reference answer have been understood and which have not. To this end, we propose an approach that splits the reference answer into its constituent propositions and two methods for detecting entailment relations between each reference answer proposition and a student response. Both methods, one using hand-crafted features and an SVM and the other using word embeddings and deep neural networks, achieve significant improvements over a state-of-the-art system and two alternative approaches.

To have a more meaningful impact, educational applications need to significantly improve the way feedback is offered to teachers and students. We propose two methods for determining propositional-level entailment relations between a reference answer and a student's response. Both methods, one using hand-crafted features and an SVM and the other using word embeddings and deep neural networks, achieve significant improvements over a state-of-the-art system and two alternative approaches.

Automatically scoring metaphor novelty has been largely unexplored, but could be of benefit to a wide variety of NLP applications. We introduce a large, publicly available metaphor novelty dataset to stimulate research in this area, and propose a regression-based approach to automatically score the novelty of potential metaphors that are expressed as word pairs. We additionally investigate which types of features are most useful for this task, and show that our approach outperforms baseline metaphor novelty scoring and standard metaphor detection approaches on this task.

The automatic generation of stimulating questions is crucial to the development of intelligent cognitive exercise applications.We developed an approach that generates appropriate Questioning the Author queries based on novel metaphors in diverse syntactic relations in literature.We show that the generated questions are comparable to human-generated questions in terms of naturalness, sensibility, and depth, and score slightly higher than human-generated questions in terms of clarity.We also show that questions generated about novel metaphors are rated as cognitively deeper than questions generated about non-or conventional metaphors, providing evidence that metaphor novelty can be leveraged to promote cognitive exercise.

Crowdsourcing offers a convenient means of obtaining labeled data quickly and inexpensively.However, crowdsourced labels are often noisier than expert-annotated data, making it difficult to aggregate them meaningfully.We present an aggregation approach that learns a regression model from crowdsourced annotations to predict aggregated labels for instances that have no expert adjudications.The predicted labels achieve a correlation of 0.594 with expert labels on our data, outperforming the best alternative aggregation method by 11.9%.Our approach also outperforms the alternatives on third-party datasets.

Attribute-based access control (ABAC) is a logical access control methodology where authorization to perform a set of operations is based on attributes of the user, the objects being accessed, the environment, and a number of other attribute sources that may be relevant to the current request. Once fully implemented within an enterprise, ABAC promotes information sharing while maintaining control of the information. However, the cost of developing ABAC policies can be a significant obstacle for organizations to migrate from traditional access control models to ABAC. Most organizations have high-level requirement specifications that define security policies and include a set of access control policies. Taking advantage of this rich source of information, we introduce a top-down policy engineering framework for ABAC that aims to automatically extract policies from unrestricted natural language documents and then, we present our methodology to extract policy related information using deep neural networks. We first create an annotated dataset comprised of 2660 sentences from real-world policy documents. We then train a deep recurrent neural network (RNN) to identify sentences containing access control policies (ACP) from irrelevant content. We applied the RNN to our new dataset as well as to five other, smaller datasets that have been employed in prior work on this task, and show that our model outperforms the state-of-the-art and leads to a performance improvement of 5.58% over the previously reported results.

Truly effective and practical educational systems will only be achievable when they have the ability to fully recognize deep relationships between a learner’s interpretation of a subject and the desired conceptual understanding. In this paper, we take important steps in this direction by introducing a new representation of sentences – Minimal Meaningful Propositions (MMPs), which will allow us to significantly improve the mapping between a learner’s answer and the ideal response. Using this technique, we make significant progress towards highly scalable and domain independent educational systems, that will be able to operate without human intervention. Even though this is a new task, we show very good results both for the extraction of MMPs and for classification with respect to their importance.

In this study, we applied a deep LSTM structure to classify dialogue acts (DAs) in open-domain conversations. We found that the word embeddings parameters, dropout regularization, decay rate and number of layers are the parameters that have the largest effect on the final system accuracy. Using the findings of these experiments, we trained a deep LSTM network that outperforms the state-of-the-art on the Switchboard corpus by 3.11%, and MRDA by 2.2%.

Training robots about the objects in their environment requires a multimodal correlation of features extracted from visual and linguistic sources. This work abstracts the task of collecting multimodal training data for object and feature learning by encapsulating it in an interactive game, I Spy, played between human players and robots. It introduces the concept of the game, briefly describes its methodology, and finally presents an evaluation of the game's performance and its appeal to human players.

OBJECTIVE: This paper introduces a model that predicts future changes in systolic blood pressure (SBP) based on structured and unstructured (text-based) information from longitudinal clinical records. METHOD: For each patient, the clinical records are sorted in chronological order and SBP measurements are extracted from them. The model predicts future changes in SBP based on the preceding clinical notes. This is accomplished using least median squares regression on salient features found using a feature selection algorithm. RESULTS: Using the prediction model, a correlation coefficient of 0.47 is achieved on unseen test data (p<.0001). This is in contrast to a baseline correlation coefficient of 0.39.

In this paper, we describe a dialogue system framework for a companionable robot, which aims to guide patients to-wards health behavior changes via natu-ral language analysis and generation. The framework involves three broad stages, rapport building and health topic identifi-cation, assess patient’s opinion of change, and designing plan and closing session. The framework uses concepts from psy-chology, computational linguistics, and machine learning and builds on them. One of the goals of the framework is to ensure that the Companionbot builds and main-tains rapport with patients. 1

This paper presents an anthropomorphic robotic bear for the exploration of human-robot interaction including verbal and non-verbal communications. This robot is implemented with a hybrid face composed of a mechanical faceplate with 10 DOFs and an LCD-display-equipped mouth. The facial emotions of the bear are designed based on the description of the Facial Action Coding System as well as some animal-like gestures described by Darwin. The mouth movements are realized by synthesizing emotions with speech. User acceptance investigations have been conducted to evaluate the likability of these facial behaviors exhibited by the eBear. Multiple Kernel Learning is proposed to fuse different features for recognizing user's facial expressions. Our experimental results show that the developed Bear-Like robot can perceive basic facial expressions and provide emotive conveyance towards human beings.

OBJECTIVE: To create annotated clinical narratives with layers of syntactic and semantic labels to facilitate advances in clinical natural language processing (NLP). To develop NLP algorithms and open source components. METHODS: Manual annotation of a clinical narrative corpus of 127 606 tokens following the Treebank schema for syntactic information, PropBank schema for predicate-argument structures, and the Unified Medical Language System (UMLS) schema for semantic information. NLP components were developed. RESULTS: The final corpus consists of 13 091 sentences containing 1772 distinct predicate lemmas. Of the 766 newly created PropBank frames, 74 are verbs. There are 28 539 named entity (NE) annotations spread over 15 UMLS semantic groups, one UMLS semantic type, and the Person semantic category. The most frequent annotations belong to the UMLS semantic groups of Procedures (15.71%), Disorders (14.74%), Concepts and Ideas (15.10%), Anatomy (12.80%), Chemicals and Drugs (7.49%), and the UMLS semantic type of Sign or Symptom (12.46%). Inter-annotator agreement results: Treebank (0.926), PropBank (0.891-0.931), NE (0.697-0.750). The part-of-speech tagger, constituency parser, dependency parser, and semantic role labeler are built from the corpus and released open source. A significant limitation uncovered by this project is the need for the NLP community to develop a widely agreed-upon schema for the annotation of clinical concepts and their relations. CONCLUSIONS: This project takes a foundational step towards bringing the field of clinical NLP up to par with NLP in the general domain. The corpus creation and NLP components provide a resource for research and application development that would have been previously impossible.

Abstract This paper describes a new method for recognizing whether a student's response to an automated tutor's question entails that they understand the concepts being taught. We demonstrate the need for a finer-grained analysis of answers than is supported by current tutoring systems or entailment databases and describe a new representation for reference answers that addresses these issues, breaking them into detailed facets and annotating their entailment relationships to the student's answer more precisely. Human annotation at this detailed level still results in substantial interannotator agreement (86.2%), with a kappa statistic of 0.728. We also present our current efforts to automatically assess student answers, which involves training machine learning classifiers on features extracted from dependency parses of the reference answer and student's response and features derived from domain-independent lexical statistics. Our system's performance, as high as 75.5% accuracy within domain and 68.8% out of domain, is very encouraging and confirms the approach is feasible. Another significant contribution of this work is that it represents a significant step in the direction of providing domain-independent semantic assessment of answers. No prior work in the area of tutoring or educational assessment has attempted to build such domain-independent systems. They have virtually all required hundreds of examples of learner answers for each new question in order to train aspects of their systems or to hand-craft information extraction templates.