Current Dialogue Research Articles

Reason, Islam and Christianity

The debate sparked off by Benedict XVI’s speech in Regensburg has not only relaunched relations between Islam and Christianity, but has also provoked an internal debate in all around the world. In these lines we offer a summary of some interventions that have appeared so far, which serve to rethink and relaunch the current dialogue between faith and reason, modernity and Christianity, non-Christian religions and the Catholic Church. In this article, we will look at the responses that emerged after the speech and other subsequent interventions, which have made this a “globalised discourse.” Rationality must be found not only in the sciences but also in philosophy, religions, ethics and art.

Patient-Centered Nursing for the Christ-Centered Nurse.

Patient-centered care (PCC), a term coined decades ago, is a key but challenging focus of nursing practice. The history, development, and current dialogue about PCC along with biblical perspectives on caregiving, wisdom, and discernment help nurses who follow Christ to offer compassionate PCC. Strategies are proposed for meaningfully engaging patients and families as active participants in healthcare. The Christian nurse can best provide consistent, effective PCC by remaining Christ-centered.

Rethinking official statistics: A sociological perspective

Does the current dialogue on the development of statistical systems provide adequate scope for transforming official statistics to deliver their social role? Can statistical systems, as currently defined, provide opportunities to people and non-state institutions to influence “what” statistics and “how” should be produced and used? This paper provides a sociological framework to investigate these questions within a broader understanding of the social functions of official statistics as part of public statistics required for a democratic society.

On-device query intent prediction with lightweight LLMs to support ubiquitous conversations

Conversational Agents (CAs) have made their way to providing interactive assistance to users. However, the current dialogue modelling techniques for CAs are predominantly based on hard-coded rules and rigid interaction flows, which negatively affects their flexibility and scalability. Large Language Models (LLMs) can be used as an alternative, but unfortunately they do not always provide good levels of privacy protection for end-users since most of them are running on cloud services. To address these problems, we leverage the potential of transfer learning and study how to best fine-tune lightweight pre-trained LLMs to predict the intent of user queries. Importantly, our LLMs allow for on-device deployment, making them suitable for personalised, ubiquitous, and privacy-preserving scenarios. Our experiments suggest that RoBERTa and XLNet offer the best trade-off considering these constraints. We also show that, after fine-tuning, these models perform on par with ChatGPT. We also discuss the implications of this research for relevant stakeholders, including researchers and practitioners. Taken together, this paper provides insights into LLM suitability for on-device CAs and highlights the middle ground between LLM performance and memory footprint while also considering privacy implications.

Can we do this online? An aesthetic inquiry on virtual studio management education

Studio methods represent one of the more recent forms of arts-based learning interventions for promoting business students' creativeness, fostering socio-material relationships, and generating new insights through its maker-orientation. Different from more individually focused analytical tools, studio practices involve the collective materialization of artifacts to study management and organization topics. The potential for introducing online studio activities remains underexplored because studio-based approaches typically rely on assumptions of physical co-presence and material matters. Contributing to the current dialogue surrounding virtual student engagement and experiential collaborations, this paper focuses on studio activities designed for engaging online MBA Organizational Behavior students in the artistic materialization of artifacts to explore their experiences with virtual groups. Realized artifacts are shared with peer groups through a virtual platform, inviting aesthetic enjoyment and interpretations. Student reflections on artifact-making and virtual sharing are examined through the lens of aesthetics to guide readers through both theoretical nuances and practical insights on co-creative studio dynamics. Participants’ narratives underscore both opportunities and challenges associated with online socio-material studio work. Implications for future research and online studio practices are considered.

Pollutants in Breast Milk: A Scoping Review of the Most Recent Data in 2024.

Perinatal exposure to pollutants, including persistent organic pollutants (POPs) and heavy metals, poses significant risks to both mothers and children, marking this period as highly vulnerable. Despite the well-acknowledged benefits of breastfeeding, there exists a gap in comprehensive understanding regarding the impact of environmental pollutants on breast milk, underscoring the critical need for this study. Our research addresses this gap by exploring the intersection of environmental health and lactation, situated within the broader 'One Health' concept, thus contributing a novel perspective to the existing body of knowledge. This scoping review aims to examine recent research on the persistent presence of organic pollutants (POPs) and heavy metals in breast milk, thereby elucidating the environmental setting's impact on milk quality. We seek to highlight the innovative angle of our study by emphasizing the 'One Health' concept, which has not been thoroughly explored in the context of lactation and environmental pollutants. We performed a scoping review, consulting two online databases to identify articles published from 1995 to 2023 that reported on pollutants in breast milk, using the PRISMA checklist. This methodological approach underlines the comprehensive and up-to-date nature of our literature review, ensuring the relevance and timeliness of our findings. From a total of 54 relevant articles, findings indicate that POPs are present in higher concentrations in breast milk the longer the lactation period. These findings highlight the persistent and bioaccumulative nature of such contaminants, offering new insights into their long-term implications for maternal and infant health. This exposure does not appear time-sensitive, suggesting pollutants accumulated in maternal fat compartments can be excreted into human milk years after exposure, a novel finding that underscores the importance of considering long-term environmental exposures in lactation research. The presence of POPs and heavy metals in both infant formula and maternal milk underscores a critical need for further comparative studies to understand the health implications better. Our discussion extends the current dialogue on the safety of breastfeeding in polluted environments, providing a new framework for assessing risks and benefits. While breastfeeding remains the WHO-recommended nutrition for optimal infant growth, the findings emphasize the importance of continued risk reduction policies to protect mothers and infants from environmental contaminants in breast milk. Our conclusion calls for an integrated approach, combining public health, environmental science, and clinical practice to develop effective strategies for reducing exposure to environmental pollutants. This multidisciplinary perspective is a significant contribution to the field, paving the way for future research and policy development.

An open intent detection model optimized for datasets based on the Bert large model

Within current task-oriented dialogue systems, the focus of intent detection predominantly centers on closed domains. Nevertheless, in real-world usage scenarios, a substantial proportion of interactions fall into the open-domain category. User intentions frequently transcend predefined boundaries, giving rise to a multitude of out-of-domain intents, which pose a formidable challenge to existing models, ultimately leading to diminished recognition rates and accuracy. The demand for open intent detection models is increasing in today's society to address this issue effectively. This paper proposes a method to optimize datasets, thereby enhancing the training accuracy of open intent detection models. Specifically, this paper employs the Adaptive Decision Boundary Learning algorithm, which is currently popular in open intent detection. Leveraging this algorithm, this paper suggests using the K-means clustering algorithm to refine the intent labels within the dataset. This process helps identify and remove outliers in the dataset, making the distinction between known domain and open-domain intent labels more precise. Experimental results on two datasets, banking77 and stackoverflow, demonstrate the effectiveness of our approach in significantly improving model accuracy.

Do It Right the First Time? Exploring the First Cross-border Acquisition and Expansion Frequency of Emerging Market Multinationals

AbstractDo it right the first time! But, how? Current dialogue on the expansion of emerging market multinational enterprises (EMNEs) is pervasive. Nonetheless, it ought to have examined strategic attributes and the speed of implementing different strategies for their first venture. Drawing on the springboard perspective, this study tests the impact of EMNEs' first cross-border acquisition (CBA) strategy and speed on their consequential expansion frequency and performance. We also examine the boundary conditions of comparative nationalism between countries, in view of the resurgence of nationalism in an era of deglobalization. Findings reveal that EMNEs' rapid adoption of a focused strategy for their first CBA increases their expansion frequency, while the adoption of a conglomerate strategy decreases it. These relationships are affected in reverse by high comparative nationalism, and the performance consequences of expansion vary with firms using different strategies for their first attempt. This study enriches the EMNE literature and highlights the role of national ideologies in international business research.

Multi-domain gate and interactive dual attention for multi-domain dialogue state tracking

Multi-domain dialogue state tracking (MDST) is a crucial component of task-oriented dialogue systems. In the context of multi-turn dialogues between the user and the system, MDST necessitates the continuous keeping track of dialogue states based on the information present in the current dialogue utterance and the dialogue states from the preceding turn. Recent work achieves the successful execution of multi-domain dialogue tasks by adopting an approach that treats each state as an individual label, while regrettably neglecting the potential benefits of incorporating domain-specific information associated with these states. Simultaneous, existing models exhibit a deficiency in effectively modelling the explicit correlations between dialogue contextual semantics and dialogue states. In this paper, we introduce the modules of multi-domain gate and interactive dual attention as novel solutions to address the aforementioned concerns. For the efficient exploitation of domain-specific information within states, we leverage the multi-domain gate as indices to amplify the states pertinent to the current utterance domain while filtering out irrelevant states. Interactive dual attention comprises utterance attention and slot attention, effectively modelling the correlation between dialogue utterances and slots. Additionally, interactive dual attention ensures that each dialogue utterance interacts with the slots once to derive all state updates, thereby ensuring computational efficiency. Specifically, slot attention models the associations between slots by incorporating semantic features to forecast updates in slot values. Meanwhile, utterance attention captures the semantics of dialogue context and integrates it with slot name features to generate dialogue states. All the aforementioned modules are designed based on a slot-independent framework, enabling efficient scalability of slots and circumventing issues related to model input limitations. The experimental results on the multi-domain dialogues dataset MultiWOZ 2.4 demonstrate the superior performance of our model compared to the baselines. Additionally, we conduct a comprehensive analysis of the effectiveness of the multi-domain gate and interactive dual attention modules, elucidating their contribution to the performance of the model through visualization and case studies.

TodBR: Target-Oriented Dialog with Bidirectional Reasoning on Knowledge Graph

Target-oriented dialog explores how a dialog agent connects two topics cooperatively and coherently, which aims to generate a “bridging” utterance connecting the new topic to the previous conversation turn. The central focus of this task entails multi-hop reasoning on a knowledge graph (KG) to achieve the desired target. However, current target-oriented dialog approaches suffer from inefficiencies in reasoning and the inability to locate pertinent key information without bidirectional reason. To address these limitations, we present a bidirectional reasoning model for target-oriented dialog implemented on a commonsense knowledge graph. Furthermore, we introduce an automated technique for constructing dialog subgraphs, which aids in acquiring multi-hop reasoning capabilities. Our experiments demonstrate that our proposed method attains superior performance in reaching the target while providing more coherent responses.

Enhancing conversational recommender systems via multi-level knowledge modeling with semantic relations

Augmenting conversational recommendation systems (CRS) with prior knowledge is crucial for learning user preferences and understanding contextual semantics. Existing methods are mainly based on knowledge graphs (KG), which rely on relationship information between entities, ignoring the semantic relationship in the dialogue. In addition, due to the limited information reflected by structured knowledge in KG, they still need broader external knowledge support to understand contextual semantics.To address the above issues, we propose a knowledge prompt-based CRS named MKCRS, which utilizes semantic relations to model multi-level knowledge and integrate it into CRS sub-tasks. Specifically, we first derive multi-level knowledge representations related to the current dialogue from external knowledge and then perform deep explicit and implicit modeling to construct knowledge prompts. To implement this framework, we design a semantic fusion model based on named entity phrases and a unified knowledge prompt generator. The former exploits explicit semantic relations between entities and contexts to enhance knowledge representations, while the latter adaptively learns the most important knowledge prompts via implicit connections between knowledge. Finally, based on the generated knowledge prompts, we construct knowledge-enhanced prompt templates for recommendation and dialogue generation tasks. Experimental results on the two datasets demonstrate that MKCRS outperforms the state-of-the-art models.

Semi-quantum dialogue scheme based on hyperentangled Bell states

Quantum dialogue enables two communicators to send their own secret information to each other, but most of the current quantum dialogue protocols have high requirements for the hardware equipment and capabilities of the communicators. In order to reduce the cost of participant, a semi-quantum dialogue scheme based on hyperentangled Bell states is studied in this paper. In our protocol, we combines the idea of semi-quantum key distribution and semi-quantum secure direct communication to realize the exchange of secret information between Alice and Bob, Alice’s information is transmitted to Bob through semi-quantum secure direct communication, while Bob’s message is extracted by Alice through semi-quantum key distribution and one-time pad encryption. This is different from the previous semi-quantum dialogue protocols, and is a new way of thinking. The security analysis shows that our protocol can not only resist the conventional attack strategies, but also has not the drawback of ‘information leakage’ problem. Compared with the existing semi-quantum dialogue protocols, it has some advantages, such as high communication efficiency, saving classical participant’s consumption and security. Although this protocol requires the preparation and analysis of hyperentangled Bell state, which are slightly more complex, it will be shown that all these are achievable by current technology. Our protocol is fully implementable, so it has good application prospects.

Green Book Revisited: Unpacking the Complexities of Race and Friendship

This study intends to investigate how friendship and race are portrayed in Peter Farrelly's Green Book movie. The study inquires whether the film depicts racism simplistically or accurately depicts the relationship between individuals from different racial backgrounds. This study's importance rests in its potential to advance the current dialogue regarding race and representation in popular culture. To respond to the research question, the researchers gathered primary sources of information from the screenplay and reviews of the movie Green Book, which were then analyzed qualitatively to identify and explore themes and characters related to race and friendship. In addition, the researchers gathered secondary data from online journals of English literature, e-books, and other sources. The information was then categorized, examined, debated, and presented to the readers. In this paper, the researchers include the historical and sociological contexts of the movie, the lives of Dr. Shirley and Tony Lip, and racism in America. The application of Critical Race Theory enriches the discussion of the movie. The study found that Green Book depicts the gradual close relationship between two different individuals from different racial backgrounds and the simplified portrayal of racism.

Mutually improved response generation and dialogue summarization for multi-domain task-oriented dialogue systems

With the development of pre-trained language models (PrLM), the research of PrLM-based multi-domain task-oriented dialogue systems (TOD) has attracted growing attention and has achieved great progress. However, most current studies suffer from two problems. First, they model dialogue state tracking as an independent subtask supervised by slot-value pairs, resulting in poor adaptability when transferring to new task domains. Second, these studies ignore the fact that not all dialogue histories are valuable for the ensuing turns as they increase in length. To tackle these two issues, we propose a simple and novel framework to explore multi-domain TOD by jointly training response generation and dialogue summarization with PrLM as the backbone. Specifically, first, we use fluent text generated by the dialogue summarization model to replace formatted dialogue states, treating the dialogue state identification task as a natural language generation task, which allows dialogue state tracking to be easily extended to new task domains. Second, dialogue summarization removes redundant and useless information from the current dialogue process and is fed into the response decoder enabling the system to focus on crucial details in long dialogues. Furthermore, we employ a dialogue chunk detector to assist the dialogue summarization model and design a fusion mechanism to dynamically integrate helpful dialogue summarization into the response generation process. Experimental results show that the proposed model achieves state-of-the-art performance in both automatic and human evaluations on two public datasets and demonstrate that the joint framework can benefit both tasks from each other.

Von „Studierenden“, „Mitarbeiter*innen“ und „Professoren“ – Geschlechtergerechte Personenbezeichnungen zwischen Varianz und Normierung

Gender-fair language has been a controversial and topical issue in Germany for over 50 years, both in public and in the professional discourse of German linguistics. In this article, two contiguous studies of gender-fair language at German universities are presented. In the first part, university guidelines for gender-fair language usage are subjected to a content analysis. In the second part, linguistic practice in the area of person reference is studied using a corpus compiled from a range of text types used in the internal and external communication of universities. The relation between the existing variance of language use and institutional attempts at standardization of gender-fair language are examined. In the two studies presented, the interplay between theory and practice becomes evident: There are tensions between theories motivating the options presented for gender-fair language and the goals formulated in university guidelines clash with the observable variation in language use. This work contributes to the current dialog on compulsory regulations on gender-fair language by providing data from writing practice while shedding light on ongoing processes of language change.

Intricacies of Relational Intimacy: Approaching the Invasive Edge with a Schizoid Patient.

In hopes of contributing to the developing self-critique of relational analysis, I offer a case that illustrates a modified approach, one informed by current dialogue. The patient described had considerable unmet selfobject needs and a schizoid sensitivity to intrusion, engendering the question explored: How do two minds meet if one is largely relationally dissociated and acutely vulnerable when integrated? A premature emphasis on intersubjectivity would have obscured the patient's narcissistic needs and reinforced her tendency to retreat. Suspending the goal of intersubjectivity allowed for a regressive process to occur, and for contact to become safe before analyst and patient paved the way for future mutual relatedness.

Dialogue Rewriting via Skeleton-Guided Generation

Dialogue rewriting aims to transform multi-turn, context-dependent dialogues into well-formed, context-independent text for most NLP systems. Previous dialogue rewriting benchmarks and systems assume a fluent and informative utterance to rewrite. Unfortunately, dialogue utterances from real-world systems are frequently noisy and with various kinds of errors that can make them almost uninformative. In this paper, we first present Real-world Dialogue Rewriting Corpus (RealDia), a new benchmark to evaluate how well current dialogue rewriting systems can deal with real-world noisy and uninformative dialogue utterances. RealDia contains annotated multi-turn dialogues from real scenes with ASR errors, spelling errors, redundancies and other noises that are ignored by previous dialogue rewriting benchmarks. We show that previous dialogue rewriting approaches are neither effective nor data-efficient to resolve RealDia. Then this paper presents Skeleton-Guided Rewriter (SGR), which can resolve the task of dialogue rewriting via a skeleton-guided generation paradigm. Experiments show that RealDia is a much more challenging benchmark for real-world dialogue rewriting, and SGR can effectively resolve the task and outperform previous approaches by a large margin.

RSP-DST: Revisable State Prediction for Dialogue State Tracking

Task-oriented dialogue systems depend on dialogue state tracking to keep track of the intentions of users in the course of conversations. Although recent models in dialogue state tracking exhibit good performance, the errors in predicting the value of each slot at the current dialogue turn of these models are easily carried over to the next turn, and unlikely to be revised in the next turn, resulting in error propagation. In this paper, we propose a revisable state prediction for dialogue state tracking, which constructs a two-stage slot value prediction process composed of an original prediction and a revising prediction. The original prediction process jointly models the previous dialogue state and dialogue context to predict the original dialogue state of the current dialogue turn. Then, in order to avoid the errors existing in the original dialogue state continuing to the next dialogue turn, a revising prediction process utilizes the dialogue context to revise errors, alleviating the error propagation. Experiments are conducted on MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.4 and results indicate that our model outperforms previous state-of-the-art works, achieving new state-of-the-art performances with 56.35, 58.09, and 75.65% joint goal accuracy, respectively, which has a significant improvement (2.15, 1.73, and 2.03%) over the previous best results.

Femmes fatales, saints, empowered women: Female biopics in Italian public television

Joining the current scholarly dialogue between biopic studies and gender/feminist/media studies, this work is meant to retrace the evolution of gender politics as expressed in the biographical genre which has played the role of the mainstay of domestic television drama since the origin of Italian broadcasting. Through a historical perspective that aims to track continuity and change over time, this work places a major focus on the 2000s, during which time women’s biographies experienced considerable growth on Italian television and more importantly began to replace traditional figures of saints and femmes fatales with female professionals in positions of leadership (journalists, politicians, entrepreneurs, scientists and artists). Based on the examination of several biopics and the close analysis of the most watched recent biopic about an ‘empowered woman’ (Luisa Spagnoli [2016]), this article seeks to explore whether the new wave of female biopics breaks crystallized generic conventions and foregrounds new representational strategies influenced by feminism or post-feminism.

Welcoming Fireside Chats to the Field of Leadership

We introduce a new commentary series at the Journal of Leadership and Organizational Studies titled Fireside Chats. The series purpose is to broaden the current dialogue on leadership research to include pieces that share wisdom and inspire ideation, nurturing and assessment of the field. Senior scholars are invited to share their views and opinions which we will feature as Fireside Chat commentaries. Their intended value is to offer a widely-accessible conversation space to reflect, create, and share wisdom.