Customer-oriented behaviour (COB) plays an important role in call centre interactions, particularly in the context of successful sales negotiation. However, the evaluation of COB in customer-agent conversations often lacks clarity in its definition and robust computational assessment methods. This paper addresses these challenges by presenting a comprehensive conceptual and empirical framework. We conducted multidimensional dialogue act annotations on authentic call centre interactions using the ISO 24617-2 taxonomy, capturing the multifaceted nature of these interactions. This process led to the identification of relevant dialogue act categories, proposed extensions concerning relationship-building aspects, and derived corpus statistics. The findings highlight specific facets of COB that positively impact on Customer Satisfaction (CS), as determined through correlation analysis. Additionally, we delved into the dependencies between COB and feedback acts, leveraging the hierarchical structure of the DIT++ model. This framework improves our understanding of the dynamics shaping sales strategies in call centres and holds promise for practical applications in optimising customer-agent interactions.

This paper introduces the corpus for the novel task of presupposition generation - a natural language generation problem where a model produces a list of presuppositions carried by the given input sentence, in the context of the presented research - given the cross-examination question. Two datasets, PECaN (Presupposition, Entailment, Contradiction and Neutral) and PGen (Presuppostion Generation), are designed to fine-tune existing BERT (CITATION) and T5 (CITATION) models for classification and generation tasks. Various corpora construction methods are proposed ranging from manual annotations, prompting the GPT 3.0 model, to augmenting data from the existing corpora. The fine-tuned models achieved high accuracy on the novel Presupposition as Natural Language Inference (PNLI) task which extends the traditional Natural Language Inference (NLI) incorporating instances of presupposition into classification. T5 outperforms BERT by broad margin achieving an overall accuracy of 84.35% compared to 71.85% of BERT, and specifically when classifying presuppositions (93% vs 73% respectively). Regarding presupposition generation, we observed that despite the limited amount of data used for fine-tuning, the model displays an emerging proficiency in generation presuppositions reaching ROUGE scores of 43.47, adhering to systematic patterns that mirror valid strategies for presupposition generation, although failed to generate the complete lists.

Self-care is essential in managing chronic diseases when patients could not always be monitored by medical staff. It therefore fills in the gap to provide patients with advice in improving their conditions in day-to-day practices. However, effectiveness of self-interventions in encouraging healthy behaviour is limited, as they are often delivered in the same manner for patients regardless of their demographics, personality and individual preferences. In this paper, we propose strategies to generate personalized health intervention messages departing from assumptions made by theories of social cognition and learning, planned behaviour and information processing. The main task is then defined personalised argument generation task. Specifically, an existing well-performing Natural Language Generation (NLG) pipeline model is extended to modulate linguistic features by ranking texts generated based on individuals’ predicted preferences for persuasive messages. Results show that the model is capable of generating diverse intervention messages while preserving the original intended meaning. The modulated interventions were approved by human evaluators as being more understandable and maintaining the same level of convincingness as human-written texts. However, the generated personalised interventions did not show significant improvements in the power to change health-related attitudes and/or behaviour compared to their non-personalised counterparts. This is attributed to the fact that human data collected for the model’s training was rather limited in size and variation.

Accurately annotated data determines whether a modern high-performing AI/ML model will present a suitable solution to a complex task/application challenge, or time and resources are wasted. The more adequate the structure of the incoming data is specified, the more efficient the data is translated to be used by the application. This paper presents an approach to an application-specific dialogue semantics design which integrates the dialogue act annotation standard ISO 24617-2 and various domain-specific semantic annotations. The proposed multi-scheme design offers a plausible and a rather powerful strategy to integrate, validate, extend and reuse existing annotations, and automatically generate code for dialogue system modules. Advantages and possible trade-offs are discussed.

For an agent, either human or artificial, to show intelligent interactive behaviour implies assessments of the reliability of own and others’ thoughts, feelings and beliefs. Agents capable of these robust evaluations are able to adequately interpret their own and others’ cognitive and emotional processes, anticipate future actions, and improve their decision-making and interactive performances across domains and contexts. Reliable instruments to assess interlocutors’ mindful capacities for monitoring and regulation - metacognition - in human-agent interaction in real-time and continuously are of crucial importance however challenging to design. The presented study reports Concurrent Think Aloud (CTA) experiments in order to access and evaluate metacognitive dispositions and attitudes of participants in human-agent interactions. A typology of metacognitive events related to the ‘verbalized’ monitoring, interpretation, reflection and regulation activities observed in a multimodal dialogue has been designed, and serves as a valid tool to identify relation between participants’ behaviour analysed in terms of ISO 24617-2 compliant dialogue acts and the corresponding metacognitive indicators.

Call centres endeavour to achieve the highest possible level of transparency with regard to the factors influencing sales success. Existing approaches to the quality assessment of customer-agent sales negotiations do not enable in-depths analysis of sales behaviour. This study addresses this gap and presents a conceptual and operational framework applying the ISO 24617-2 dialogue act annotation scheme, a multidimensional taxonomy of interoperable semantic concepts. We hypothesise that the ISO 24617-2 dialogue act annotation framework adequately supports sales negotiation assessment in the domain of call centre conversations. Authentic call centre conversations are annotated and a range of extensions/modifications are proposed making the annotation scheme better fit this new domain. We concluded that ISO 24617-2 serves as a powerful instrument for the analysis and assessment of sales negotiation and strategies applied by a call centre agent.

The paper presents ongoing efforts in design of a typology of metacognitive events observed in a multimodal dialogue. The typology will serve as a tool to identify relations between participants’ dispositions, dialogue actions and metacognitive indicators. It will be used to support an assessment of metacognitive knowledge, experiences and strategies of dialogue participants. Based on the mutidimensional dialogue model defined within the framework of Dynamic Interpretation Theory and ISO 24617-2 annotation standard, the proposed approach provides a systematic analysis of metacognitive events in terms of dialogue acts, i.e. concepts that dialogue research community is used to operate on in dialogue modelling and system design tasks.

The paper presents a discourse-based approach to the analysis of argumentative texts departing from the assumption that the coherence of a text should capture argumentation structure as well and, therefore, existing discourse analysis tools can be successfully applied for argument segmentation and annotation tasks. We tested the widely used Penn Discourse Tree Bank full parser (Lin et al., 2010) and the state-of-the-art neural network NeuralEDUSeg (Wang et al., 2018) and XLNet (Yang et al., 2019) models on the two-stage discourse segmentation and discourse relation recognition. The two-stage approach outperformed the PDTB parser by broad margin, i.e. the best achieved F1 scores of 21.2 % for PDTB parser vs 66.37% for NeuralEDUSeg and XLNet models. Neural network models were fine-tuned and evaluated on the argumentative corpus showing a promising accuracy of 60.22%. The complete argument structures were reconstructed for further argumentation mining tasks. The reference Dagstuhl argumentative corpus containing 2,222 elementary discourse unit pairs annotated with the top-level and fine-grained PDTB relations will be released to the research community.

The paper presents a novel discourse-based approach to argument quality assessment defined as a graph classification task, where the depth of reasoning (argumentation) is evident from the number and type of detected discourse units and relations between them. We successfully applied state-of-the-art discourse parsers and machine learning models to reconstruct argument graphs with the identified and classified discourse units as nodes and relations between them as edges. Then Graph Neural Networks were trained to predict the argument quality assessing its acceptability, relevance, sufficiency and overall cogency. The obtained accuracy ranges from 74.5% to 85.0% and indicates that discourse-based argument structures reflect qualitative properties of natural language arguments. The results open many interesting prospects for future research in the field of argumentation mining.

ISO standard 24617-2 for dialogue act annotation, established in 2012, has in the past few years been used both in corpus annotation and in the design of components for spoken and multimodal dialogue systems. This has brought some inaccuracies and undesirbale limitations of the standard to light, which are addressed in a proposed second edition. This second edition allows a more accurate annotation of dependence relations and rhetorical relations in dialogue. Following the ISO 24617-4 principles of semantic annotation, and borrowing ideas from EmotionML, a triple-layered plug-in mechanism is introduced which allows dialogue act descriptions to be enriched with information about their semantic content, about accompanying emotions, and other information, and allows the annotation scheme to be customised by adding application-specific dialogue act types.

Effective, professional and socially competent dialogue of health care providers with their patients is essential to best practice in medicine. To identify, categorize and quantify salient features of patient-provider communication, to model interactive processes in medical encounters and to design digital interactive medical services, two important instruments have been developed: (1) medical interaction analysis systems with the Roter Interaction Analysis System (RIAS) as the most widely used by medical practitioners and (2) dialogue act annotation schemes with ISO 24617-2 as a multidimensional taxonomy of interoperable semantic concepts widely used for corpus annotation and dialogue systems design. Neither instrument fits all purposes. In this paper, we perform a systematic comparative analysis of the categories defined in the RIAS and ISO taxonomies. Overcoming the deficiencies and gaps that were found, we propose a number of extensions to the ISO annotation scheme, making it a powerful analytical and modelling instrument for the analysis, modelling and assessment of medical communication.

The processing of medical information is not a trivial task for medical non-experts. The paper presents an artificial assistant designed to facilitate a reliable access to medical online contents. Interactions are modelled as doctor-patient Question Answering sessions within a pre-operative patient education scenario where the system addresses patient’s information needs explaining medical events and procedures. This implies an accurate medical information extraction from and reasoning with available medical knowledge and large amounts of unstructured multilingual online data. Bridging the gap between medical knowledge and data, we explore a language-agnostic approach to medical concepts mining from the standard terminologies, and the data-driven collection of the corresponding seed terms in a distant supervision setting for German. Experimenting with different terminologies, features and term matching strategies, we achieved a promising F-score of 0.91 on the medical term extraction task. The concepts and terms are used to search and retrieve definitions from the verified online free resources. The proof-of-concept definition retrieval system is designed and evaluated showing promising results, acceptable by humans in 92% of cases.

This paper describes a method to automatically create dialogue resources annotated with dialogue act information by reusing existing dialogue corpora. Numerous dialogue corpora are available for research purposes and many of them are annotated with dialogue act information that captures the intentions encoded in user utterances. Annotated dialogue resources, however, differ in various respects: data collection settings and modalities used, dialogue task domains and scenarios (if any) underlying the collection, number and roles of dialogue participants involved and dialogue act annotation schemes applied. The presented study encompasses three phases of data-driven investigation. We, first, assess the importance of various types of features and their combinations for effective cross-domain dialogue act classification. Second, we establish the best predictive model comparing various cross-corpora training settings. Finally, we specify models adaptation procedures and explore late fusion approaches to optimize the overall classification decision taking process. The proposed methodology accounts for empirically motivated and technically sound classification procedures that may reduce annotation and training costs significantly.

The paper describes experimental dialogue data collection activities, as well semantically annotated corpus creation undertaken within EU-funded METALOGUE project(www.metalogue.eu). The project aims to develop a dialogue system with flexible dialogue management to enable system’s adaptive, reactive, interactive and proactive dialogue behavior in setting goals, choosing appropriate strategies and monitoring numerous parallel interpretation and management processes. To achieve these goals negotiation (or more precisely multi-issue bargaining) scenario has been considered as the specific setting and application domain. The dialogue corpus forms the basis for the design of task and interaction models of participants negotiation behavior, and subsequently for dialogue system development which would be capable to replace one of the negotiators. The METALOGUE corpus will be released to the community for research purposes.

This paper presents the DialogBank, a new language resource consisting of dialogues with gold standard annotations according to the ISO 24617-2 standard. Some of these dialogues have been taken from existing corpora and have been re-annotated according to the ISO standard; others have been annotated directly according to the standard. The ISO 24617-2 annotations have been designed according to the ISO principles for semantic annotation, as formulated in ISO 24617-6. The DialogBank makes use of three alternative representation formats, which are shown to be interoperable.

This paper explores a way of achieving interoperability: developing a query format for accessing existing annotated corpora whose expressions make use of the annotation language defined by the standard. The interpretation of expressions in the query implements a mapping from ISO 24617-2 concepts to those of the annotation scheme used in the corpus. We discuss two possible ways to query existing annotated corpora using DiAML. One way is to transform corpora into DiAML compliant format, and subsequently query these data using XQuery or XPath. The second approach is to define a DiAML query that can be directly used to retrieve requested information from the annotated data. Both approaches are valid. The first one presents a standard way of querying XML data. The second approach is a DiAML-oriented querying of dialogue act annotated data, for which we designed an interface. The proposed approach is tested on two important types of existing dialogue corpora: spoken two-person dialogue corpora collected and annotated within the HCRC Map Task paradigm, and multiparty face-to-face dialogues of the AMI corpus. We present the results and evaluate them with respect to accuracy and completeness through statistical comparisons between retrieved and manually constructed reference annotations.

This paper describes the data collection and annotation carried out within the DBOX project ( Eureka project, number E! 7152). This project aims to develop interactive games based on spoken natural language human-computer dialogues, in 3 European languages: English, German and French. We collect the DBOX data continuously. We first start with human-human Wizard of Oz experiments to collect human-human data in order to model natural human dialogue behaviour, for better understanding of phenomena of human interactions and predicting interlocutors actions, and then replace the human Wizard by an increasingly advanced dialogue system, using evaluation data for system improvement. The designed dialogue system relies on a Question-Answering (QA) approach, but showing truly interactive gaming behaviour, e.g., by providing feedback, managing turns and contact, producing social signals and acts, e.g., encouraging vs. downplaying, polite vs. rude, positive vs. negative attitude towards players or their actions, etc. The DBOX dialogue corpus has required substantial investment. We expect it to have a great impact on the rest of the project. The DBOX project consortium will continue to maintain the corpus and to take an interest in its growth, e.g., expand to other languages. The resulting corpus will be publicly released.

Subtitling and audiovisual translation have been recognized as areas that could greatly benefit from the introduction of Statistical Machine Translation (SMT) followed by post-editing, in order to increase efficiency of subtitle production process. The FP7 European project SUMAT (An Online Service for SUbtitling by MAchine Translation: http://www.sumat-project.eu) aims to develop an online subtitle translation service for nine European languages, combined into 14 different language pairs, in order to semi-automate the subtitle translation processes of both freelance translators and subtitling companies on a large scale. In this paper we discuss the data collection and parallel corpus compilation for training SMT systems, which includes several procedures such as data partition, conversion, formatting, normalization and alignment. We discuss in detail each data pre-processing step using various approaches. Apart from the quantity (around 1 million subtitles per language pair), the SUMAT corpus has a number of very important characteristics. First of all, high quality both in terms of translation and in terms of high-precision alignment of parallel documents and their contents has been achieved. Secondly, the contents are provided in one consistent format and encoding. Finally, additional information such as type of content in terms of genres and domain is available.

Recent years have witnessed a growing interest in annotating linguistic data at the semantic level, including the annotation of dialogue corpus data. The annotation scheme developed as international standard for dialogue act annotation ISO 24617-2 is based on the DIT++ scheme (Bunt, 2006; 2009) which combines the multidimensional DIT scheme (Bunt, 1994) with concepts from DAMSL (Allen and Core , 1997) and various other schemes. This scheme is designed in a such way that it can be applied not only to spoken dialogue, as is the case for most of the previously defined dialogue annotation schemes, but also to multimodal dialogue. This paper describes how the ISO 24617-2 annotation scheme can be used, together with the DIT++ method of ‘multidimensional segmentation', to annotate nonverbal and multimodal dialogue behaviour. We analyse the fundamental distinction between (a) the coding of surface features; (b) form-related semantic classification; and (c) semantic annotation in terms of dialogue acts, supported by experimental studies of (a) and (b). We discuss examples of specification languages for representing the results of each of these activities, show how dialogue act annotations can be attached to XML representations of functional segments of multimodal data.

This paper summarizes the latest, final version of ISO standard 24617-2 ``Semantic annotation framework, Part 2: Dialogue acts"""". Compared to the preliminary version ISO DIS 24617-2:2010, described in Bunt et al. (2010), the final version additionally includes concepts for annotating rhetorical relations between dialogue units, defines a full-blown compositional semantics for the Dialogue Act Markup Language DiAML (resulting, as a side-effect, in a different treatment of functional dependence relations among dialogue acts and feedback dependence relations); and specifies an optimally transparent XML-based reference format for the representation of DiAML annotations, based on the systematic application of the notion of `ideal concrete syntax'. We describe these differences and briefly discuss the design and implementation of an incremental method for dialogue act recognition, which proves the usability of the ISO standard for automatic dialogue annotation.

This paper shows how interoperable dialogue act annotations, using the multidimensional annotation scheme and the markup language DiAML of ISO standard 24617-2, can conveniently be obtained using the newly implemented facility in the ANVIL annotation tool to produce XML-based output directly in the DiAML format. ANVIL offers the use of multiple user-defined `tiers' for annotating various kinds of information. This is shown to be convenient not only for multimodal information but also for dialogue act annotation according to ISO standard 24617-2 because of the latter's multidimensionality: functional dialogue segments are viewed as expressing one or more dialogue acts, and every dialogue act belongs to one of a number of dimensions of communication, defined in the standard, for each of which a different ANVIL tier can conveniently be used. Annotations made in the multi-tier interface can be exported in the ISO 24617-2 format, thus supporting the creation of interoperable annotated corpora of multimodal dialogue.

Recent years witness a growing interest in the use of multimodal data for modelling of communicative behaviour in dialogue. Dybkjaer and Bernsen (2002), point out that coding schemes for multimodal data are used solely by their creators. Standardisation has been achieved to some extent for coding behavioural features for certain nonverbal expressions, e.g. for facial expression, however, for the semantic annotation of such expressions combined with other modalities such as speech there is still a long way to go. The majority of existing dialogue act annotation schemes that are designed to code semantic and pragmatic dialogue information are limited to analysis of spoken modality. This paper investigates the applicability of existing dialogue act annotation schemes to the semantic annotation of multimodal data, and the way a dialogue act annotation scheme can be extended to cover dialogue phenomena from multiple modalities. The general conclusion of our explorative study is that a multidimensional dialogue act taxonomy is usable for this purpose when some adjustments are made. We proposed a solution for adding these aspects to a dialogue act annotation scheme without changing its set of communicative functions, in the form of qualifiers that can be attached to communicative function tags.

This paper describes an ISO project which aims at developing a standard for annotating spoken and multimodal dialogue with semantic information concerning the communicative functions of utterances, the kind of semantic content they address, and their relations with what was said and done earlier in the dialogue. The project, ISO 24617-2 ""Semantic annotation framework, Part 2: Dialogue acts"", is currently at DIS stage. The proposed annotation schema distinguishes 9 orthogonal dimensions, allowing each functional segment in dialogue to have a function in each of these dimensions, thus accounting for the multifunctionality that utterances in dialogue often have. A number of core communicative functions is defined in the form of ISO data categories, available at http://semantic-annotation.uvt.nl/dialogue-acts/iso-datcats.pdf; they are divided into ""dimension-specific"" functions, which can be used only in a particular dimension, such as Turn Accept in the Turn Management dimension, and ""general-purpose"" functions, which can be used in any dimension, such as Inform and Request. An XML-based annotation language, ""DiAML"" is defined, with an abstract syntax, a semantics, and a concrete syntax.

In this paper the dialogue act annotation of naive and expert annotators, both annotating the same data, are compared in order to characterise the insights annotations made by different kind of annotators may provide for evaluating dialogue act tagsets. It is argued that the agreement among naive annotators provides insight in the clarity of the tagset, whereas agreement among expert annotators provides an indication of how reliably the tagset can be applied when errors are ruled out that are due to deficiencies in understanding the concepts of the tagset, to a lack of experience in using the annotation tool, or to little experience in annotation more generally. An indication of the differences between the two groups in terms of inter-annotator agreement and tagging accuracy on task-oriented dialogue in different domains, annotated with the DIT++ dialogue act tagset is presented, and the annotations of both groups are assessed against a gold standard. Additionally, the effect of the reduction of the tagset’s granularity on the performances of both groups is looked into. In general, it is concluded that the annotations of both groups provide complementary insights in reliability, clarity, and more fundamental conceptual issues.

Semantic roles have often proved to be useful labels for stating linguistic generalisations of various sorts. There is, however, a lack of agreement on their defining criteria, which causes serious problems for semantic roles to be a useful classificatory device for predicate-argument relations. These criteria should (a) support the design of a semantic role set which is complete but does not contain redundant relations; (b) be based on semantic rather than morphological, lexical or syntactic properties; and (c) enable formal interpretation. In this paper we report on the analyses of alternative approaches to annotation and representation of semantic role information (such as FrameNet, PropBank and VerbNet) with respect to their models of description, granularity of semantic role sets, definitions of semantic roles concepts, consistency and reliability of annotations. We present methodological principles for characterising well-defined concepts which were developed within the LIRICS (Linguistic InfRastructure for Interoperable ResourCes and Systems; see http://lirics.loria.fr) project, as well as the designed set of semantic roles and their definitions in ISO 12620 format. We discuss evaluation results of the defined concepts for semantic role annotation concerning the redundancy and completeness of the tagset and the reliability of annotations in terms of inter-annotator agreement.