Analyzing large conversational datasets is often inefficient due to the linear nature of text, which hinders the tracking of interaction evolution over time. To address this, we present FlowDisco, an interactive platform for the automatic discovery and exploration of dialogue flows. The framework uses semantic embeddings and modular clustering to transform raw text into probabilistic dialogue flows. By providing a web interface with dynamic filtering and a suite of analytical metrics, FlowDisco simplifies the visual identification and validation of conversational behaviors at scale. The platform’s utility is demonstrated through real-world application scenarios, including customer support interactions and multi-party political debates, where it successfully uncovers complex patterns and sentiment shifts that traditional sequential analysis often overlooks.

Analyzing how large-scale multi-party dialogues shape collective behavior is a central challenge in computational linguistics. However, traditional text-based methods often overlook the complex, non-linear turn-taking dynamics defining these interactions. To address this gap, we propose a framework based on Dialogue Action Flows (DAFs) that integrates verbal utterances and non-verbal actions into a unified probabilistic representation of interactional behavior. Interactions are encoded as speaker-action states, forming a probabilistic DAF that reveals dominant behavioral trajectories and recurrent patterns. We validate this framework on five years of Portuguese Parliament debates. Analysis reveals systematic behavioral asymmetries driven by party roles: while government parties exhibit increasing alignment, opposition forces, particularly the radical wing, maintain persistently high conflict. Additionally, the rising volume of interactions across legislative years indicates a progressively heated environment. Overall, our framework provides a quantitative and interpretable approach for modeling polarization, alignment, and interactional dynamics in multi-party political discourse.

Customer-support services increasingly rely on automation, whether fully or with human intervention. Despite optimising resources, this may result in mechanical protocols and lack of human interaction, thus reducing customer loyalty. Our goal is to enhance interpretability and provide guidance in communication through novel tools for easier analysis of message trends and sentiment variations. Monitoring these contributes to more informed decision-making, enabling proactive mitigation of potential issues, such as protocol deviations or customer dissatisfaction. We propose a generic approach for dialogue flow discovery that leverages clustering techniques to identify dialogue states, represented by related utterances. State transitions are further analyzed to detect prevailing sentiments. Hence, we discover sentiment-aware dialogue flows that offer an interpretability layer to artificial agents, even those based on black-boxes, ultimately increasing trustworthiness. Experimental results demonstrate the effectiveness of our approach across different dialogue datasets, covering both human-human and human-machine exchanges, applicable in task-oriented contexts but also to social media, highlighting its potential impact across various customer-support settings.

Several dialogue corpora are currently available for research purposes, but they still fall short for the growing interest in the development of dialogue systems with their own specific requirements. In order to help those requiring such a corpus, this paper surveys a range of available options, in terms of aspects like speakers, size, languages, collection, annotations, and domains. Some trends are identified and possible approaches for the creation of new corpora are also discussed.

Having in mind the lack of work on the automatic recognition of verbal humour in Portuguese, a topic connected with fluency in a natural language, we describe the creation of three corpora, covering two styles of humour and four sources of non-humorous text, that may be used for related studies. We then report on some experiments where the created corpora were used for training and testing computational models that exploit content and linguistic features for humour recognition. The obtained results helped us taking some conclusions about this challenge and may be seen as baselines for those willing to tackle it in the future, using the same corpora.

We present AIA-BDE, a corpus of 380 domain-oriented FAQs in Portuguese and their variations, i.e., paraphrases or entailed questions, created manually, by humans, or automatically, with Google Translate. Its aims to be used as a benchmark for FAQ retrieval and automatic question-answering, but may be useful in other contexts, such as the development of task-oriented dialogue systems, or models for natural language inference in an interrogative context. We also report on two experiments. Matching variations with their original questions was not trivial with a set of unsupervised baselines, especially for manually created variations. Besides high performances obtained with ELMo and BERT embeddings, an Information Retrieval system was surprisingly competitive when considering only the first hit. In the second experiment, text classifiers were trained with the original questions, and tested when assigning each variation to one of three possible sources, or assigning them as out-of-domain. Here, the difference between manual and automatic variations was not so significant.

This paper proposes a new topic model that exploits word sense information in order to discover less redundant and more informative topics. Word sense information is obtained from WordNet and the discovered topics are groups of synsets, instead of mere surface words. A key feature is that all the known senses of a word are considered, with their probabilities. Alternative configurations of the model are described and compared to each other and to LDA, the most popular topic model. However, the obtained results suggest that there are no benefits of enriching LDA with word sense information.