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Child's understanding and intentions are not always easy to read in drawings. This is particularly evident in the type of drawings we refer to as 'analytical drawings'. In order to understand analytical drawings made by preschool children, it is important to have a dialogue with the young authors. In our study, we examined the ability of students of pedagogy study programmes to interpret an example of a preschooler's analytic drawing where the intentions are not readily apparent. Using the example of an analytical drawing of a six-year-old girl, we designed a questionnaire that was administered to 226 students of the Faculty of Education at the University of Ljubljana. Using the questionnaire, we evaluated the students' ability to interpret the content and the child's intentions expressed in the analytical drawing, as well as their opinions about what they would find most useful to help them to interpret the girl's drawing. The results confirmed our hypothesis that it is impossible to understand, interpret, and evaluate these types of children's analytic drawings without a dialogue with a child. [ABSTRACT FROM AUTHOR]

Tsetlin Machines (TM) use finite state machines for learning and propositional logic to represent patterns. The resulting pattern recognition approach captures information in the form of conjunctive clauses, thus facilitating human interpretation. In this work, we propose a TM‐based approach to three common natural language processing (NLP) tasks, namely, sentiment analysis, semantic relation categorization and identifying entities in multi‐turn dialogues. By performing frequent itemset mining on the TM‐produced patterns, we show that we can obtain a global and a local interpretation of the learning, one that mimics existing rule‐sets or lexicons. Further, we also establish that our TM based approach does not compromise on accuracy in the quest for interpretability, via comparison with some widely used machine learning techniques. Finally, we introduce the idea of a relational TM, which uses a logic‐based framework to further extend the interpretability. [ABSTRACT FROM AUTHOR]

This paper proposes a method for analyzing the dialogical functions of metaphors in communicative interactions, and more specifically in the context of medical interviews. The dialogical goals proposed and pursued by the interlocutors are coded using a coding scheme that captures seven mutually exclusive categories of dialogical moves. The functions of the moves, including metaphors, can be identified and correlated with other variables relevant to the type of communication under analysis. The coding scheme is used to analyze a corpus of 39 interactions between healthcare providers and patients affected by Type 2 diabetes. The exploratory quantitative analysis, for the purpose of determining the different distributions of metaphor uses between patients and providers, is combined with qualitative analysis in which the thematic areas of the metaphors are considered. The findings show how patients and providers use metaphors for pursuing different dialogical goals and meeting distinct communicative needs. [ABSTRACT FROM AUTHOR]

Task-oriented dialogue systems need to take appropriate actions not only for clear user requests but also for ambiguous and vague ones. In this study, "ambiguous" denotes that although users have potential requests, they failed to clearly define and verbalize their content and conditions which can be associated with system actions. For such ambiguous requests, taking reflective actions is one plausible choice for such systems. In our study, "reflective" denotes taking actions that satisfy user requests before the users themselves clarify their demands. We constructed such a reflective dialogue agent by collecting a corpus that includes pairs of ambiguous user requests and corresponding reflective system actions on sightseeing navigation with a smartphone. Since annotating every possible combination of user requests and system actions is impossible, this study built a corpus where one reflective action is annotated to one ambiguous user request. To train an action selection model on such incomplete training data in which only one action is associated with a request, we applied the positive/unlabeled (PU) learning method, which assumes that only part of the data is labeled with positive examples. In addition, we enhanced the action selection by extracting and distilling knowledge that corresponds to causality from the training data using a causality detection model. The experimental results show that both the PU learning method and the causality detection model improved the performances of the reflective action selection compared to the conventional positive/negative (PN) learning method. • We built a reflective action selection system given ambiguous user requests. • We collected a dialogue corpus that bridges ambiguous requests to reflective actions. • We explored whether several actions can be reflective for one request on test data. • We applied the PU learning method to deal with partially annotated training data. • We distilled causality knowledge to utilize in the reflective action selection. [ABSTRACT FROM AUTHOR]

As knowledge-grounded dialogue systems are attracting intense research interest, technology that facilitates reference to various types of external knowledge as dialogue-system data is also developing. A knowledge-grounded dialogue system must be capable of (1) accurately interpret the conversation content, (2) determining whether external knowledge should be referenced for the current turn, (3) identifying the external knowledge to be referenced, and (4) generating a response. If the referenced external knowledge is in the form of a question–answer pair, this pair is closely related to the domain to which the external knowledge belongs and the entity to which it pertains. This study proposes a knowledge-grounded dialogue system that predicts the domain and entity associated with a question–answer pair referenced by the dialogue system; the system then leverages this information to effectively implement the above three external-knowledge-based capabilities. As the dialogue data associated with external knowledge is diverse, adaptation to the slots and entities of different dialogue datasets is challenging. In this study, the Triple Copy (TripPy) model, which is one of the leading benchmark models for dialogue-state tracking with the Multi-domain Wizard-of-Oz (MultiWOZ) dataset, is further developed to adapt to DSTC10 data for the external knowledge-based dialogue system; hence, dialogue content is effectively interpreted. The developed knowledge-grounded dialogue system incorporates knowledge-seeking turn detection, knowledge selection, and knowledge-grounded response generation models. The model, in DSTC10 dataset, achieves 15.49%p, 12.54%p, and 36.45%p improvements over the baseline, the state-of-the-art model for the previous version of the data (DSTC9 dataset), in terms of the F1 score, Recall@1 score, and BLEU-1 score, respectively. Moreover, in a dialogue-state tracking task for DSTC10 dataset, a 15.41%p improvement in joint goal accuracy score is achieved compared to the TripPy model. • A goal-oriented dialogue system using external knowledge to respond to a user is proposed. • Adapting a dialogue-state tracking model to targeting datasets to well understand a conversation. • The advanced knowledge-grounded dialogue system using the extracted entities and tracked domain. [ABSTRACT FROM AUTHOR]

Currently, task-oriented dialogue systems that perform specific tasks based on dialogue are widely used. Moreover, research and development of non-task-oriented dialogue systems are also actively conducted. One of the problems with these systems is that it is difficult to switch topics naturally. In this study, we focus on interview dialogue systems. In an interview dialogue, the dialogue system can take the initiative as an interviewer. The main task of an interview dialogue system is to obtain information about the interviewee via dialogue and to assist this individual in understanding his or her personality and strengths. In order to accomplish this task, the system needs to be flexible and appropriate for detecting topic switching and topic breaks. Given that topic switching tends to be more ambiguous in interview dialogues than in task-oriented dialogues, existing topic modeling methods that determine topic breaks based only on relationships and similarities between words are likely to fail. In this study, we propose a method for detecting topic breaks in dialogue to achieve flexible topic switching in interview dialogue systems. The proposed method is based on multi-task learning neural network that uses embedded representations of sentences to understand the context of the text and utilizes the intention of an utterance as a feature. In multi-task learning, not only topic breaks but also the intention associated with the utterance and the speaker are targets of prediction. The results of our evaluation experiments show that using utterance intentions as features improves the accuracy of topic separation estimation compared to the baseline model. [ABSTRACT FROM AUTHOR]

International higher-education systems position teachers in ways that require an ecological perspective to understand the complexities of identity formation. Accordingly, we apply a model of legitimisation that combines Positioning Theory and Bourdieu's analogy of capitals, means/codes, and marketplaces to investigate the roles, identities, and investments of six university teachers in Thailand. Findings from in-depth interviews show that our participants navigate the complexity and fluidity of their positions through a view of identity that puts holistic learning above content or language-related goals. Moreover, despite positioning themselves as individuals, their discourses reflect a common goal for ethical self-formation driven by emotion labour. • Explores the identities, positions, and investments of six English language teachers in an internationalised department. • Advances a method for exploring identity agency that considers legitimisation from an ecological perspective. • Investigates context-culture-specific emotional discourses in language teachers as they strive for ethical self-formations. • Shows how teachers can legitimise preferred positions through investments that reflect their level of agency. • Illustrates the contrasting approaches of experienced teachers, who perform similar roles through varying means/codes. [ABSTRACT FROM AUTHOR]