An Open-Domain Dialog Act Taxonomy

This document defines the taxonomy of dialog acts that are necessary to encode domain-independent dialog moves in the context of a task-oriented, open-domain dialog. Such taxonomy is formulated to satisfy two complementary requirements: on the one hand, domain independence, i.e. the power to cover all the range of possible interactions in any type of conversation (particularly conversation oriented to the performance of tasks). On the other hand, the ability to instantiate a concrete set of tasks as defined by a specific knowledge base (such as an ontology of domain concepts and actions) and within a particular language. For the modeling of dialog acts, inspiration is taken from several well-known dialog annotation schemes, such as DAMSL (Core & Allen, 1997), TRAINS (Traum, 1996) and VERBMOBIL (Alexandersson et al., 1997)

Advanced techniques for personalized, interactive question answering

Using a computer to answer questions has been a human dream since the beginning of the digital era. A first step towards the achievement of such an ambitious goal is to deal with naturallangilage to enable the computer to understand what its user asks. The discipline that studies the conD:ection between natural language and the represen~ tation of its meaning via computational models is computational linguistics. According to such discipline, Question Answering can be defined as the task that, given a question formulated in natural language, aims at finding one or more concise answers in the form of sentences or phrases. Question Answering can be interpreted as a sub-discipline of information retrieval with the added challenge of applying sophisticated techniques to identify the complex syntactic and semantic relationships present in text. Although it is widely accepted that Question Answering represents a step beyond standard infomiation retrieval, allowing a more sophisticated and satisfactory response to the user's information needs, it still shares a series of unsolved issues with the latter. First, in most state-of-the-art Question Answering systems, the results are created independently of the questioner's characteristics, goals and needs. This is a serious limitation in several cases: for instance, a primary school child and a History student may need different answers to the questlon: When did, the Middle Ages begin? Moreover, users often issue queries not as standalone but in the context of a wider information need, for instance when researching a specific topic. Although it has recently been proposed that providing Question Answering systems with dialogue interfaces would encourage and accommodate the submission of multiple related questions and handle the user's requests for clarification, interactive Question Answering is still at its early stages: Furthermore, an i~sue which still remains open in current Question Answering is that of efficiently answering complex questions, such as those invoking definitions and descriptions (e.g. What is a metaphor?). Indeed, it is difficult to design criteria to assess the correctness of answers to such complex questions. .. These are the central research problems addressed by this thesis, and are solved as follows. An in-depth study on complex Question Answering led to the development of classifiers for complex answers. These exploit a variety of lexical, syntactic and shallow semantic features to perform textual classification using tree-~ernel functions for Support Vector Machines. The issue of personalization is solved by the integration of a User Modelling corn': ponent within the the Question Answering model. The User Model is able to filter and fe-rank results based on the user's reading level and interests. The issue ofinteractivity is approached by the development of a dialogue model and a dialogue manager suitable for open-domain interactive Question Answering. The utility of such model is corroborated by the integration of an interactive interface to allow reference resolution and follow-up conversation into the core Question Answerin,g system and by its evaluation. Finally, the models of personalized and interactive Question Answering are integrated in a comprehensive framework forming a unified model for future Question Answering research

La categorización como habilidad para organizar la información en Educación Infantil a través de una segunda lengua (inglés)

El presente trabajo se centra en el empleo de la categorización como medio de organización de la información en edades tempranas. Esta habilidad de pensamiento fue trabajada mediante un rincón de actividad. La lengua inglesa es utilizada como medio de comunicación entre los alumnos y el docente. Dicha práctica fue llevada a cabo en un colegio que seguía la metodología AICLE.Grado en Educación Infanti

INTRODUCING RESET PATTERNS: AN EXTENSION TO A RAPID DIALOGUE PROTOTYPING METHODOLOGY

This paper exposes the Rapid Dialogue Prototyping Methodology [1, 2, 3], a methodology allowing the easy and automatic derivation of an ad hoc dialogue management system from a specific task description. The goal of the produced manager is to provide the user with a dialogue based interface to easily perform the target task. In addition, reset patterns, an extension of the prototyping methodology allowing a more flexible interaction with the user, are proposed in order to improve the efficiency of the dialogue. Reset patterns are justified and theoretically validated by the definition of an average gain function to optimize. Two approaches to such an optimization are presented, focusing on a different aspect of the gain function. Eventually, experimental results are presented and a conclusion is drawn on the usefulness of the new feature

Foundations of Data Science : a comprehensive overview formed at the 1st International Symposium on the Science of Data Science

We present a summary of the 1st International Symposium on the Science of Data Science, organized in Summer 2021 as a satellite event of the 8th Swiss Conference on Data Science held in Lucerne, Switzerland. We discuss what establishes the scientific core of the discipline of data science by introducing the corresponding research question, providing a concise overview of relevant related prior work, followed by a summary of the individual workshop contributions. Finally, we expand on the common views which were formed during the extensive workshop discussions

Effect of a quality improvement program on compliance to the sepsis bundle in non-ICU patients: a multicenter prospective before and after cohort study

ObjectiveSepsis and septic shock are major challenges and economic burdens to healthcare, impacting millions of people globally and representing significant causes of mortality. Recently, a large number of quality improvement programs focused on sepsis resuscitation bundles have been instituted worldwide. These educational initiatives have been shown to be associated with improvements in clinical outcomes. We aimed to evaluate the impact of a multi-faceted quality implementing program (QIP) on the compliance of a “simplified 1-h bundle” (Sepsis 6) and hospital mortality of severe sepsis and septic shock patients out of the intensive care unit (ICU).MethodsEmergency departments (EDs) and medical wards (MWs) of 12 academic and non-academic hospitals in the Lombardy region (Northern Italy) were involved in a multi-faceted QIP, which included educational and organizational interventions. Patients with a clinical diagnosis of severe sepsis or septic shock according to the Sepsis-2 criteria were enrolled in two different periods: from May 2011 to November 2011 (before-QIP cohort) and from August 2012 to June 2013 (after-QIP cohort).Measurements and main resultsThe effect of QIP on bundle compliance and hospital mortality was evaluated in a before–after analysis. We enrolled 467 patients in the before-QIP group and 656 in the after-QIP group. At the time of enrollment, septic shock was diagnosed in 50% of patients, similarly between the two periods. In the after-QIP group, we observed increased compliance to the “simplified rapid (1 h) intervention bundle” (the Sepsis 6 bundle – S6) at three time-points evaluated (1 h, 13.7 to 18.7%, p = 0.018, 3 h, 37.1 to 48.0%, p = 0.013, overall study period, 46.2 to 57.9%, p < 0.001). We then analyzed compliance with S6 and hospital mortality in the before- and after-QIP periods, stratifying the two patients’ cohorts by admission characteristics. Adherence to the S6 bundle was increased in patients with severe sepsis in the absence of shock, in patients with serum lactate <4.0 mmol/L, and in patients with hypotension at the time of enrollment, regardless of the type of admission (from EDs or MWs). Subsequently, in an observational analysis, we also investigated the relation between bundle compliance and hospital mortality by logistic regression. In the after-QIP cohort, we observed a lower in-hospital mortality than that observed in the before-QIP cohort. This finding was reported in subgroups where a higher adherence to the S6 bundle in the after-QIP period was found. After adjustment for confounders, the QIP appeared to be independently associated with a significant improvement in hospital mortality. Among the single S6 procedures applied within the first hour of sepsis diagnosis, compliance with blood culture and antibiotic therapy appeared significantly associated with reduced in-hospital mortality.ConclusionA multi-faceted QIP aimed at promoting an early simplified bundle of care for the management of septic patients out of the ICU was associated with improved compliance with sepsis bundles and lower in-hospital mortality

Linguistic kernels for answer re-ranking in question answering systems

Abstract Answer selection is the most complex phase of a Question Answering (QA) system. To solve this task, typical approaches use unsupervised methods such as computing the similarity between query and answer, optionally exploiting advanced syntactic, semantic or logic representations. In this paper, we study supervised discriminative models that learn to select (rank) answers using examples of question and answer pairs. The pair representation is implicitly provided by kernel combinations applied to each of its members. To reduce the burden of large amounts of manual annotation, we represent question and answer pairs by means of powerful generalization methods, exploiting the application of structural kernels to syntactic/semantic structures. We experiment with Support Vector Machines and string kernels, syntactic and shallow semantic tree kernels applied to part-of-speech tag sequences, syntactic parse trees and predicate argument structures on two datasets which we have compiled and made available. Our results on classification of correct and incorrect pairs show that our best model improves the bag-of-words model by 63% on a TREC dataset. Moreover, such a binary classifier, used as a re-ranker, improves the Mean Reciprocal Rank of our baseline QA system by 13%. These findings demonstrate that our method automatically selects an appropriate representation of question-answer relations