Improving Conversational Passage Re-ranking with View Ensemble

This paper presents ConvRerank, a conversational passage re-ranker that employs a newly developed pseudo-labeling approach. Our proposed view-ensemble method enhances the quality of pseudo-labeled data, thus improving the fine-tuning of ConvRerank. Our experimental evaluation on benchmark datasets shows that combining ConvRerank with a conversational dense retriever in a cascaded manner achieves a good balance between effectiveness and efficiency. Compared to baseline methods, our cascaded pipeline demonstrates lower latency and higher top-ranking effectiveness. Furthermore, the in-depth analysis confirms the potential of our approach to improving the effectiveness of conversational search.Comment: SIGIR 202

Tracking Context in Conversational Search: From Utterances to Neural Embeddings

The use of conversational assistants is becoming increasingly more popular among the general public, pushing the research towards more advanced and sophisticated techniques. Hence, there are currently a number of research opportunities to extend the comprehension and applicability of these tasks in everyday systems. These conversational assistants are capable of performing various tasks, such as chitchatting, internal device functions (e.g., setting up an alarm), and searching for information. In the last few years, the interest in conversational search is increasing, not only because of the generalization of conversational assistants but also because conversational search is a step forward in allowing a more natural interaction with the system. To build a system such as this, many components need to work together, since in a conversation, the importance of context is paramount to retrieve the best answers to the user’s questions. In this thesis, the focus was on developing a conversational search system that aims to help people search for information in a natural way. In particular, this system must be able to understand the context where the question is posed, tracking the current state of the conversation and detecting mentions to previous questions and answers. We achieve this by using a context-tracking component based on neural query-rewriting models. Another crucial aspect of the system is to provide the most relevant answers given the question and the conversational history. To achieve this objective, we used state-of-the-art retrieval and re-ranking methods and expanded their architecture to use the conversational context. The results obtained with the system developed achieved state-of-the-art when compared to the baselines present in TREC Conversational Assistance Track (CAsT) 2019.O uso de assistentes conversacionais está a tornar-se cada vez mais popular entre o público em geral, levando à investigação de técnicas mais avançadas e sofisticadas. Consequentemente, existem atualmente várias oportunidades de investigação para estender a compreensão e aplicabilidade destas tarefas em sistemas do quotidiano. Estes assistentes são capazes de efetuar várias tarefas como, por exemplo: ter uma conversa informal, efetuar funções internas ao dispositivo (e.g. colocar um alarme), e pesquisar por informação. Nos últimos anos, o interesse em pesquisa conversacional tem estado a aumentar, não só pela generalização dos assistentes conversacionais, mas também devido a ser um passo em frente para permitir uma interação mais natural com o sistema. Para construir um sistema deste tipo, vários componentes têm de trabalhar em conjunto, uma vez que numa conversa o contexto é da maior importância para recuperar as melhores respostas para as perguntas do utilizador. Nesta tese, o foco foi desenvolver um sistema de pesquisa conversacional para ajudar as pessoas a pesquisar por informação de uma forma natural. Em particular, este sistema tem de ser capaz de compreender o contexto onde a questão é colocada, fazendo tracking do estado atual da conversa e detetando menções a perguntas e respostas anteriores. Com esse objetivo, desenvolvemos um componente de tracking de contexto baseado em modelos neuronais de reescrita de perguntas. Outro aspeto crucial deste sistema é fornecer as respostas mais relevantes dada uma pergunta e o histórico da conversa. Para alcançar este objetivo, utilizámos modelos do estado-da-arte em recuperação de informação e re-ranking e expandimos estas arquiteturas de modo a utilizarem o contexto da conversa. Os resultados obtidos com o sistema desenvolvido atingiram resultados do estado.da-arte quando comparados às baselines submetidas no TREC Conversational Assistance Track (CAsT) 2019

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction