Query Resolution for Conversational Search with Limited Supervision

In this work we focus on multi-turn passage retrieval as a crucial component of conversational search. One of the key challenges in multi-turn passage retrieval comes from the fact that the current turn query is often underspecified due to zero anaphora, topic change, or topic return. Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution. In this paper, we model the query resolution task as a binary term classification problem: for each term appearing in the previous turns of the conversation decide whether to add it to the current turn query or not. We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We propose a distant supervision method to automatically generate training data by using query-passage relevance labels. Such labels are often readily available in a collection either as human annotations or inferred from user interactions. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrieval architecture and demonstrate its effectiveness on the TREC CAsT dataset.Comment: SIGIR 2020 full conference pape

Reply With: Proactive Recommendation of Email Attachments

Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message. Analysis of an enterprise email corpus reveals that 35% of the time when users include these items as part of their response, the attachable item is already present in their inbox or sent folder. A modern email client can proactively retrieve relevant attachable items from the user's past emails based on the context of the current conversation, and recommend them for inclusion, to reduce the time and effort involved in composing the response. In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user. As email search systems are commonly available, we constrain the recommendation task to formulating effective search queries from the context of the conversations. The query is submitted to an existing IR system to retrieve relevant items for attachment. We also present a novel strategy for generating labels from an email corpus---without the need for manual annotations---that can be used to train and evaluate the query formulation model. In addition, we describe a deep convolutional neural network that demonstrates satisfactory performance on this query formulation task when evaluated on the publicly available Avocado dataset and a proprietary dataset of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 201

Contextualizing and Expanding Conversational Queries without Supervision

Most conversational passage retrieval systems try to resolve conversational dependencies by using an intermediate query resolution step. To do so, they synthesize conversational data or assume the availability of large-scale question rewriting datasets. To relax those conditions, we propose a zero-shot unified resolution–retrieval approach, that (i) contextualizes and (ii) expands query embeddings using the conversation history and without fine-tuning on conversational data. Contextualization biases the last user question embeddings towards the conversation. Query expansion is used in two ways: (i) abstractive expansion generates embeddings based on the current question and previous history, whereas (ii) extractive expansion tries to identify history term embeddings based on attention weights from the retriever. Our experiments demonstrate the effectiveness of both contextualization and unified expansion in improving conversational retrieval. Contextualization does so mostly by resolving anaphoras to the conversation and bringing their embeddings closer to the important resolution terms that were omitted. By adding embeddings to the query, expansion targets phenomena of ellipsis more explicitly, with our analysis verifying its effectiveness on identifying and adding important resolutions to the query. By combining contextualization and expansion, we find that our zero-shot unified resolution–retrieval methods are competitive and can even outperform supervised methods

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

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system