From Research to Reality: Evaluation of a Single-Computer Real-Time LVCSR System for Speech-Based Retrieval

This paper presents a series of tests that were performed on a state-of-the-art real-time automatic speech recognition system for English, in a single-computer implementation. As the intention is to use the system for speech-based query-free document retrieval in conversations, several parameters were varied: text type, microphone quality, computing power, speaker fluency, and pace of the speech. Word accuracy over various word counts, including a restriction to content words, varied in the 30%-70% range. The paper compares results over many conditions, and concludes that the ASR system is acceptable for the intended use only if all the parameters are in optimal conditions. If more than two parameters are suboptimal, then its output becomes too noisy for document retrieval

Query Refinement Using Conversational Context: a Method and an Evaluation Resource

This paper introduces a query refinement method applied to queries asked by users during a meeting or a conversation. Current approaches suffer from poor quality to achieve this goal, but we argue that their performance could be improved by focusing on the local context of the conversation. The proposed technique first represents the local context by extracting keywords from the transcript of the conversation. It then expands the queries with keywords that best represent the topic of the query (e.g. pairs of expansion keywords together with a weight indicating their topical similarity to the query). Moreover, we present a dataset called AREX and an evaluation metric. We compared our query expansion approach with other methods, on topics extracted from the AREX dataset and based on relevance judgments collected in a crowdsourcing experiment. The comparisons indicate the superiority of our method on both manual and ASR transcripts of the AMI Meeting Corpus

Une Approche de recommandation proactive dans un environnement mobile

Les systèmes de recommandation contextuelle visent à combiner un ensemble de technologies et de connaissances sur le contexte de l’utilisateur pour lui fournir une information pertinente au moment où il en a le plus besoin, c’est ce qu’on appelle la recommandation proactive. Dans cet article nous proposons une approche de recommandation contextuelle et proactive dans un environnement mobile qui apprend implicitement les préférences de l’utilisateur. Nous avons évalué notre approche dans le cadre de la tâche “Contextual Suggestion Track” de TREC 2014. Les résultats que nous avons obtenus sont prometteurs

Similarity Learning Over Large Collaborative Networks

In this thesis, we propose novel solutions to similarity learning problems on collaborative networks. Similarity learning is essential for modeling and predicting the evolution of collaborative networks. In addition, similarity learning is used to perform ranking, which is the main component of recommender systems. Due to the the low cost of developing such collaborative networks, they grow very quickly, and therefore, our objective is to develop models that scale well to large networks. The similarity measures proposed in this thesis make use of the global link structure of the network and of the attributes of the nodes in a complementary way. We first define a random walk model, named Visiting Probability (VP), to measure proximity between two nodes in a graph. VP considers all the paths between two nodes collectively and thus reduces the effect of potentially unreliable individual links. Moreover, using VP and the structural characteristics of small-world networks (a frequent type of networks), we design scalable algorithms based on VP similarity. We then model the link structure of a graph within a similarity learning framework, in which the transformation of nodes to a latent space is trained using a discriminative model. When trained over VP scores, the model is able to better predict the relations in a graph in comparison to models learned directly from the network’s links. Using the VP approach, we explain how to transfer knowledge from a hypertext encyclopedia to text analysis tasks. We consider the graph of Wikipedia articles with two types of links between them: hyperlinks and content similarity ones. To transfer the knowledge learned from the Wikipedia network to text analysis tasks, we propose and test two shared representation methods. In the first one, a given text is mapped to the corresponding concepts in the network. Then, to compute similarity between two texts, VP similarity is applied to compute the distance between the two sets of nodes. The second method uses the latent space model for representation, by training a transformation from words to the latent space over VP scores. We test our proposals on several benchmark tasks: word similarity, document similarity / clustering / classification, information retrieval, and learning to rank. The results are most often competitive compared to state-of-the-art task-specific methods, thus demonstrating the generality of our proposal. These results also support the hypothesis that both types of links over Wikipedia are useful, as the improvement is higher when both are used. In many collaborative networks, different link types can be used in a complementary way. Therefore, we propose two joint similarity learning models over the nodes’ attributes, to be used for link prediction in networks with multiple link types. The first model learns a similarity metric that consists of two parts: the general part, which is shared between all link types, and the specific part, which is trained specifically for each type of link. The second model consists of two layers: the first layer, which is shared between all link types, embeds the objects of the network into a new space, and then a similarity is learned specifically for each link type in this new space. Our experiments show that the proposed joint modeling and training frameworks improve link prediction performance significantly for each link type in comparison to multiple baselines. The two-layer similarity model outperforms the first one, as expected, due to its capability of modeling negative correlations among different link types. Finally, we propose a learning to rank algorithm on network data, which uses both the attributes of the nodes and the structure of the links for learning and inference. Link structure is used in training through a neighbor-aware ranker which considers both node attributes and scores of neighbor nodes. The global link structure of the network is used in inference through an original propagation method called the Iterative Ranking Algorithm. This propagates the predicted scores in the graph on condition that they are above a given threshold. Thresholding improves performance, and makes a time-efficient implementation possible, for application to large scale graphs. The observed improvements are explained considering the structural properties of small-world networks

A Speech-based Just-in-Time Retrieval System using Semantic Search

The Automatic Content Linking Device is a just-in-time document retrieval system which monitors an ongoing conversation or a monologue and enriches it with potentially related documents, including multimedia ones, from local repositories or from the Internet. The documents are found using keyword-based search or using a semantic similarity measure between documents and the words obtained from automatic speech recognition. Results are displayed in real time to meeting participants, or to users watching a recorded lecture or conversation

Modeling Users' Information Needs in a Document Recommender for Meetings

People are surrounded by an unprecedented wealth of information. Access to it depends on the availability of suitable search engines, but even when these are available, people often do not initiate a search, because their current activity does not allow them, or they are not aware of the existence of this information. Just-in-time retrieval brings a radical change to the process of query-based retrieval, by proactively retrieving documents relevant to users' current activities, in an easily accessible and non-intrusive manner. This thesis presents a novel set of methods intended to improve the relevance of a just-in-time retrieval system, specifically a document recommender system designed for conversations, in terms of precision and diversity of results. Additionally, we designed an evaluation protocol to compare the proposed methods in the thesis with other ones using crowdsourcing. In contrast to previous systems, which model users' information needs by extracting keywords from clean and well-structured texts, this system models them from the conversation transcripts, which contain noise from automatic speech recognition (ASR) and have a free structure, often switching between several topics. To deal with these issues, we first propose a novel keyword extraction method which preserves both the relevance and the diversity of topics of the conversation, to properly capture possible users' needs with minimum ASR noise. Implicit queries are then built from these keywords. However, the presence of multiple unrelated topics in one query introduces significant noise into the retrieval results. To reduce this effect, we separate users' needs by topically clustering keyword sets into several subsets or implicit queries. We introduce a merging method which combines the results of multiple queries which are prepared from users' conversation to generate a concise, diverse and relevant list of documents. This method ensures that the system does not distract its users from their current conversation by frequently recommending them a large number of documents. Moreover, we address the problem of explicit queries that may be asked by users during a conversation. We introduce a query refinement method which leverages the conversation context to answer the users' information needs without asking for additional clarifications and therefore, again, avoiding to distract users during their conversation. Finally, we implemented the end-to-end document recommender system by integrating the ideas proposed in this thesis and then proposed an evaluation scenario with human users in a brainstorming meeting