This paper describes a corpus consisting of real-world dialogues in English between users and a task-oriented conversational agent, with interactions revolving around the description of finite state automata. The creation of this corpus is part of a larger research project aimed at developing tools for an easier access to educational content, especially in STEM fields, for users with visual impairments. The development of this corpus was precisely motivated by the aim of providing a useful resource to support the design of such tools. The core feature of this corpus is that its creation involved both sighted and visually impaired participants, thus allowing for a greater diversity of perspectives and giving the opportunity to identify possible differences in the way the two groups of participants interacted with the agent. The paper introduces this corpus, giving an account of the process that led to its creation, i.e. the methodology followed to obtain the data, the annotation scheme adopted, and the analysis of the results. Finally, the paper reports the results of a classification experiment on the annotated corpus, and an additional experiment to assess the annotation capabilities of three large language models, in view of a further expansion of the corpus.

Neural networks are notoriously data-hungry.This represents an issue in cases where data are scarce such as in low-resource languages.Data augmentation is a technique commonly used in computer vision to provide neural networks with more data and increase their generalization power.When dealing with data augmentation for natural language, however, simple data augmentation techniques similar to the ones used in computer vision such as rotation and cropping cannot be employed because they would generate ungrammatical texts.Thus, data augmentation needs a specific design in the case of neural logic-to-text systems, especially for a structurally rich input format such as the ones used for meaning representation.This is the case of the neural natural language generation for Discourse Representation Structures (DRS-to-Text), where the logical nature of DRS needs a specific design of data augmentation.In this paper, we adopt a novel approach in DRS-to-Text to selectively augment a training set with new data by adding and varying two specific lexical categories, i.e. proper and common nouns.In particular, we propose using WordNet supersenses to produce new training sentences using both in-and out-of-context nouns.We present a number of experiments for evaluating the role played by augmented lexical information.The experimental results prove the effectiveness of our approach for data augmentation in DRS-to-Text generation.Exp.No Implementation Type Precision Recall F1-Score 01 Gold (without augmentation) 95.2 95.4 95.3 02 Gold + PN (inside context) 95.8 95.9 95.8 03 Gold + PN (outside context) 95.9 95.9 95.9 04 Gold + CN (inside context with SS) 95.7 95.7 95.7 05 Gold + CN (inside context without SS) 95.7 95.5 95.6 06 Gold + CN (outside context with SS) 95.5 95.8 95.7 07 Gold + CN (outside context without SS) 95.8 95.7 95.7 08 Gold + PN-with-CN 96.1 95.9

In this article, we investigate the case of human-machine dialogues in the specific domain of commercial customer care. We built a corpus of conversations between users and a customer-care chatbot of an Italian Telecom Company, focusing on a sample of conversations where users contact the service asking for explanations about billing issues or overcharges. We observed that users’ requests are often vague, generic or incomprehensible. In such cases, commercial dialogue systems typically ask for clarifications or further details to fully understand users’ specific requests. However, from the corpus analysis it appeared that chatbot's clarifying requests may result in ineffective interactions, with users eventually giving up the conversation or switching to a human agent for a faster query resolution. A recovery strategy is thus needed to anticipate users’ information needs, or intentions. We address this issue resorting to GEN-DS, a dialogue system based on symbolic data-to-text generation. GEN-DS analyzes the user-company contextual relational knowledge, with the aim to generate more relevant answers to unclear questions. In this article, we describe the GEN-DS architecture along with the experiments we carried out to evaluate its output. Results from an offline human evaluation show significant improvements of GEN-DS compared to the original system. These improvements concern properties such as utility, necessity, understandability, and quickness of the information communicated in the dialogue. We believe that GEN-DS techniques may find application in all the dialogue systems that need to manage vague requests and must rely on relational knowledge.

Artificial intelligence largely relies on bounds on differences (BoDs) to model binary constraints regarding different dimensions, such as time, space, costs, and calories. Recently, some approaches have extended the BoDs framework in a fuzzy, “noncrisp” direction, considering probabilities or preferences. While previous approaches have mainly aimed at providing an optimal solution to the set of constraints, we propose an innovative class of approaches in which constraint propagation algorithms aim at identifying the “space of solutions” (i.e., the minimal network) with their preferences, and query answering mechanisms are provided to explore the space of solutions as required, for example, in decision support tasks. Aiming at generality, we propose a class of approaches parametrized over user-defined scales of qualitative preferences (e.g., Low, Medium, High, and Very High), utilizing the resume and extension operations to combine preferences, and considering different formalisms to associate preferences with BoDs. We consider both “general” preferences and a form of layered preferences that we call “pyramid” preferences. The properties of the class of approaches are also analyzed. In particular, we show that, when the resume and extension operations are defined such that they constitute a closed semiring, a more efficient constraint propagation algorithm can be used. Finally, we provide a preliminary implementation of the constraint propagation algorithms.

This paper presents a project about the automatic generation of persuasive messages in the context of the diet management.In the first part of the paper we introduce the basic mechanisms related to data interpretation and content selection for a numerical data-to-text generation architecture.In the second part of the paper we discuss a number of factors influencing the design of the messages.In particular, we consider the design of the aggregation procedure.Finally, we present the results of a human-based evaluation concerning this design factor.

Now-related temporal data play an important role in many applications. Clifford et al.'s approach is a milestone to model the semantics of `now' in temporal relational databases. Several relational representation models for now-related data have been presented; however, the semantics of such representations has not been explicitly studied. Additionally, the definition of a relational algebra to query now-related data is an open problem. We propose the first integrated approach that provides both a neat semantics for now-related data and a compact 1NF representation (data model and relational algebra) for them. Additionally, our approach also extends current approaches to consider (i) domains where it is not always possible to know when changes in the world are recorded in the database and (ii) now-related data with a bound on their persistency in the future. To do so, we explicitly model the notion of temporal indeterminacy in the future for now-related data. The properties of our approach are also analyzed both from a theoretical (semantic correctness and reducibility of the algebra) and from an experimental point of view. Experiments show that, despite the fact that our approach is a major extension to current temporal relational approaches, no significant overhead is added to deal with `now'.

The cooperative construction of data/knowledge bases has recently had a significant impulse (see, e.g., Wikipedia [1]). In cases in which data/knowledge quality and reliability are crucial, proposals of update/insertion/deletion need to be evaluated by experts. To the best of our knowledge, no theoretical framework has been devised to model the semantics of update proposal/ evaluation in the relational context. Since time is an intrinsic part of most domains (as well as of the proposal/evaluation process itself), semantic approaches to temporal relational databases (specifically, Bitemporal Conceptual Data Model (henceforth, BCDM) [2]) are the starting point of our approach. In this paper, we propose BCDMPV, a semantic temporal relational model that extends BCDM to deal with multiple update/insertion/deletion proposals and with acceptances/rejections of proposals themselves. We propose a theoretical framework, defining the new data structures, manipulation operations and temporal relational algebra and proving some basic properties, namely that BCDMPVis a consistent extension of BCDM and that it is reducible to BCDM. These properties ensure consistency with most relational temporal database frameworks, facilitating implementations.

Valid-time indeterminacy is "don't know when" indeterminacy, coping with cases in which one does not exactly know when a fact holds in the modeled reality. In this paper, we first propose a reference representation (data model and algebra) in which all possible temporal scenarios induced by valid-time indeterminacy can be extensionally modeled. We then specify a family of 16 more compact representational data models. We demonstrate their correctness with respect to the reference representation and analyze several properties, including their data expressiveness. Then, we compare these compact models along several relevant dimensions. Finally, we also extend the reference representation and a representative of compact representations to cope with probabilities.

Valid-time indeterminacy concerns not knowing exactly when a fact holds in the modeled reality. In this paper, we first propose a reference approach (data model and algebra) in which all possible temporal scenarios induced by valid-time indeterminacy can be extensionally modeled. We then specify a family of sixteen more compact representational data models. We demonstrate their correctness with respect to the reference approach and analyze several properties, including their data expressiveness and correctness with respect to the reference approach. Finally, we compare these compact models along several relevant dimensions.

In this paper, we propose a framework for representing and reasoning about qualitative and quantitative temporal constraints between periodic events. In particular, our contribution is twofold: (i) we provide a formalism to deal with both qualitative and quantitative “periodicity-dependent” constraints between repeated events, considering user-defined periodicities as well; and (ii) we propose an intensional approach to temporal reasoning, which is based on the operations of intersection and composition. Such a comprehensive approach, to the best of our knowledge, represents an innovative contribution that integrates and extends results from both the artificial intelligence and the temporal databases literature. © 2009 Wiley Periodicals, Inc.

Several explanation and interpretation tasks, such as diagnosis, plan recognition and image interpretation, can be formalized as abductive reasoning. A number of approaches, including recent ones [1, 4], address the problem based on a task-independent representation of a domain which includes an ontology or taxonomy of hypotheses. In this paper we adopt a similar representation, but we also deal with abduction as an iterative process where, like in model-based diagnosis, further observations are proposed to discriminate among candidate explanations; in addition, we take into account costs of observations and actions. In fact, discrimination also involves refining hypotheses, but this is performed down to an appropriate level which depends on the cost of actions (e.g. repair actions or therapy) to be taken based on the results of abduction, and on the cost of additional observations, which should be balanced with the benefits, in terms of more suitable actions, of better discrimination. The presence of a domain representation with abstractions has a significant impact on this trade-off. In general, a better assessment of the situation at hand, based on additional observations, leads to a more focused action. However, the cost of observing the same phenomenon at different levels of abstraction may vary significantly; in fact, it could involve more or less costly medical or technical tests, or computationally complex image processing, possibly with additional costs due to the delay before taking an action. Moreover, the knowledge base could have been designed independently of the explanation/action task (e.g. diagnosis and repair), and could therefore include a detailed description of the domain which is not necessary for the task; more generally, the convenience of a detailed discrimination may depend on the specific case at hand. By explicitly considering abstractions in the iterative abduction process, we can often reduce the observation costs significantly, yet maintaining the ability to exploit detailed observations and knowledge when convenient (similar advantages have been shown in inductive classification with abstractions, e.g. [6]). In the following, we first describe the knowledge we expect to be available. We then describe a basic iterative abduction loop and, finally, we concentrate on the criterion for selecting the next step in the loop: either performing a next observation at some level of detail, or stopping because the estimated most convenient choice is performing the action(s) associated with the current hypotheses.

In many application areas, including planning, workflow, guideline and protocol management, the description of the domain involves composite and/or periodic events, mutually related by temporal constraints on the execution order. Such events represent ‘classes’, since they can be instantiated to specific executions of the plan, guideline etc., and each execution must ‘respect’ the temporal constraints imposed on the corresponding classes. The main goal of our work is to propose an approach dealing with the above-mentioned temporal phenomena. To achieve such an objective, the authors propose a tractable domain-independent temporal reasoner. This enhances the generality of our approach, which provides a domain-independent module that can be integrated with other software tools to solve temporal problems in specific domains. From the methodological point of view, the authors first devise a representation formalism coping with the aforesaid phenomena, and then they describe temporal constraint propagation algorithms to deal with constraint inheritance and to perform temporal consistency checking. The representation formalism has been designed carefully, to obtain algorithms that are both complete and tractable. Finally, the paper also shows experimental results, including an application of the authors’ approach to clinical guidelines, evaluating their impact on future applications and research activities.

Representing and reasoning with repeated and periodic events is important in many real-world domains, such as protocol and guideline management. In this set, it is important to give support to complex periodicities, that can involve non-symmetric repetitions, imprecision, variability, pauses between repetitions, and nested time intervals. Also, in these domains it can be useful to give support to composite events, as well as classes of events (i.e. types of actions) and instances of events (i.e. specific actions). In this paper, we propose a general-purpose domain-independent knowledge server dealing with all these issues. In particular, we describe a compact and (hopefully) user-friendly formalism for representing repetition/periodicity temporal constraints that supports arbitrarily nested repetitions as well as possibly imprecise and variable delays between repetitions. Moreover, we define two algorithms for performing consistency checking on knowledge bases of (possibly repeated/periodic) classes and instances of events retaining the efficiency of less expressive approaches.

Reasoning with temporal constraints is a ubiquitous issue in many computer science tasks, for which many dedicated approaches have been and are being built. In particular, in many areas, including planning, workflow, guidelines, and protocol management, one needs to represent and reason with temporal constraints between classes of events (e.g., between the types of actions needed to achieve a goal) and temporal constraints between instances of events (e.g., between the specific actions being executed). The temporal constraints between the classes of events must be inherited by the instances, and the consistency of both types of constraints must be checked. In this article, we design a general-purpose domain-independent knowledge server dealing with these issues. In particular, we propose a formalism to represent temporal constraints, and we point out two orthogonal parameters that affect the definition of reasoning algorithms operating on them. We then show four algorithms to deal with inheritance and to perform temporal consistency checking (depending on the parameters) and we study their properties. Finally, we report the results we obtained by applying our system to the treatment of temporal constraints in clinical guidelines. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 919–947, 2004.

In many application areas, including planning, workflow, guidelines and protocol management, the description of the domain requires the use of part-of relations between events, the modeling of periodic repetitions and the treatment of "standard" temporal constraints between such events. Events in plans, workflows etc. represent "classes", in the sense that they can be instantiated to specific executions of the plan guideline, etc. Of course, such executions must respect (i.e., be consistent with) the temporal constraints explicitly or implicitly (e.g., by the part-of relation) conveyed by the class descriptions. In this paper, we propose a tractable domain-independent temporal server dealing with the above phenomena. We first sketch a representation formalism coping with part-of and instance-of relations, periodicity and temporal constraints, and then we describe two algorithms to deal with inheritance and to perform temporal consistency checking.