Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

Peer reviewe

Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)

1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020 Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

Lie-o-matic: using natural language processing to detect contradictory statements

A Era da Informação trouxe consigo a digitalização de dados e, consequentemente, um rápido, e com maior alcance, fluxo de informação e produção da mesma. Pessoas como jornalistas têm dificuldade em lidar com a crescente divulgação de dados e em monitorar e aprovar a informação propagada, que poderá estar corrompida (conter mentiras, inconsistências, contradições, etc.). Considerando este problema atual e a constante evolução em técnicas de processamento de linguagem natural e \textit{"machine learning"}, estamos interessados em tirar vantagens desses recentes desenvolvimentos para atacar o caso específico de deteção de contradições em texto. Esta dissertação investiga o efeito de vários conjuntos de dados, de diferentes domínios e tarefas (como contradições em diferentes contextos ou argumentos de suporte e ataque), no desempenho da aprendizagem de um modelo de classificação de aprendizagem supervisionada. Assim, nós abordamos o problema como uma tarefa de classificação binária, afinando uma tarefa de classificação de pares de frases, desenvolvida sob um modelo BERT pré-treinado, para depois executarmos previsões de se dois textos são contraditórios ou não. Estudos em deteção de contradições têm-se focado mais em distinguir antónimos e palavras contrastantes. Tanto quanto é do nosso conhecimento, nenhuma investigação sistemática alguma vez considerou \textit{"transfer learning"} (transferência de conhecimento) para a tarefa de detetar contradições. Para ilustrarmos a nossa ideia, contradições no domínio político foram usadas com caso de estudo. Como estamos a testar transferência de conhecimento, conduzimos experiências usando como domínio de tarefa de origem dados retirados de quatro corpos disponíveis ao público: MultiNLI, US2016, Argumentative Microtext e Argument Annotated Essays. Para o domínio alvo, criamos dois conjuntos de dados contendo pares de contradições provenientes de duas origens diferentes, um artigo online expondo aclamações contraditórias do Donald Trump e o corpo MultiNLI (mas só os exemplos do género governamental). Para avaliar as experiências guiadas, medimos o desempenho da classificação maioritariamente a partir de análises à curva característica de operação (curva ROC) e à curva de Precisão-Abrangência. Os resultados dos estudos respondem à pergunta de estudo de que, de facto, outros conjuntos de dados podem ser usados para melhorar o desempenho da aprendizagem de um modelo de inferência sobre uma tarefa alvo, embora os resultados não serem significantes o suficiente para assegurarmos firmemente a consistência e confiança dos mesmos. Os resultados dão ideias de que tipo de relações entre documentos se deve priorizar caso se recorra a transferência de conhecimento para detetar contradições. Nós concluímos que o tipo de tarefa, o contexto e os padrões de linguagem (marcas linguísticas características do discurso de uma pessoa) têm um maior impacto e, por isso, podem ser uteis quando diferentes dados contêm semelhanças a nível destes três fatores. Não obstante, no nosso estudo enfrentamos limitações, como a falta de robustez no conjunto de dados para teste construído a partir das contradições do Donald Trump, porque não recorremos a anotadores profissionais, e o facto de os resultados de classificação alcançados já serem muito bons apenas usando o conjunto de dados alvo para treino e teste, o que nos deixa com pouca margem para melhorias.The Information age brought the digitization of data, and, consequently, a faster and wider flow and production of information. People, such as journalists, struggle to cope with the increasing data disclosure and to monitor and verify the spread information, that might be corrupted (containing lies, inconsistencies, contradictions, etc.). Considering this current problem and the constant evolution in Natural Language Processing (NLP) techniques and machine learning, we are interested in taking advantage of those recent developments to tackle the specific NLP task of detecting contradictions in text. This dissertation investigates the effect of various datasets, from different domains and tasks (like contradictions in a different context or arguments of support and attack), on the learning performance of a supervised learning classification model for detecting contradictions. Hence, we address the problem as a binary classification task, fine-tuning a sentence-pair classification task, built on top of a pre-trained BERT model, to later run prediction of if two texts are contradictory or not. Literature on contradiction detection has focused almost on separating antonyms and contrasting words. To the best of our knowledge, no systematic investigation has considered transfer learning for the task of contradiction detection. To illustrate this idea, contradictions in a political domain were used as a case study. Since we are testing transfer learning, we conducted experiments using as source task domain data collected from four publicly available corpora: MultiNLI, US2016, Argumentative Microtext, and Argument Annotated Essays. Then, for target domain, we built two datasets containing pairs of contradictions from two different sources, an online article exposing Donald Trump contradictory claims, and MultiNLI corpus (but only instances of government genre). To evaluate the conducted experiments, we measure classification performances mainly through ROC and Precision-Recall curves analysis. The findings from the research answer our research question that, indeed, other datasets can be used to boost an inference model learning performance on a target task, although the results are not too significant to strongly assure the consistency and reliability of our findings. The findings offer insights into what kind of relationship between documents one should focus on when resorting to transfer learning for detection of contradictions. We conclude that the domain's task, context and language patterns (linguistic markers characteristic of a person speech), have a bigger impact and, thus, can be helpful if different data contains similarities in these three factors. Nonetheless, we faced some limitations in our research, such as the lack of robustness in the testing dataset built from Donald Trump contradictions, because of missing professional annotators for that task, and the already great classification results when only using the target domain for training and testing, leading to a small margin for improvements

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)