This is an old revision of the document! Match-Tensor: a Deep Relevance Model for Search

The application of Deep Neural Networks for ranking in search engines may obviate the need for the extensive feature engineering common to current learning-to-rank methods. However, we show that combining simple relevance matching features like BM25 with existing Deep Neural Net models often substantially improves the accuracy of these models, indicating that they do not capture essential local relevance matching signals. We describe a novel deep Recurrent Neural Net-based model that we call Match-Tensor. The architecture of the Match-Tensor model simultaneously accounts for both local relevance matching and global topicality signals allowing for a rich interplay between them when computing the relevance of a document to a query. On a large held-out test set consisting of social media documents, we demonstrate not only that Match-Tensor outperforms BM25 and other classes of DNNs but also that it largely subsumes signals present in these models. An Approach for Weakly-Supervised Deep Information Retrieval

We present an approach for generating weak supervision training data for use in a neural IR model. Specifically, we use a news corpus with article headlines acting as pseudo-queries and article content as pseudo-documents, and we propose a measure of interaction similarity to filter these pseudo-documents Relevance-based Word Embedding

The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. DSRIM: A Deep Neural Information Retrieval Model Enhanced by a Knowledge Resource Driven Representation of Documents

Guided by the intuition that the relational semantics might improve the effectiveness of deep neural approaches, we propose the Deep Semantic Resource Inference Model (DSRIM) that relies on: 1) a representation of raw-data that models the relational semantics of text by jointly considering objects and relations expressed in a knowledge resource, and 2) an end-to-end neural architecture that learns the query-document relevance by leveraging the distributional and relational semantics of documents and queries. The experimental evaluation carried out on two TREC datasets from TREC Terabyte and TREC CDS tracks relying respectively on WordNet and MeSH resources, indicates that our model outperforms state-of-the-art semantic and deep neural IR models. Learning to Effectively Select Topics For Information Retrieval Test Collections Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization

we treat weight sharing as a flexible mechanism for incorporating prior knowledge into neural models. We show that this approach consistently yields improved performance on classifi- cation tasks compared to baseline strategies that do not exploit weight sharing. Automatic Question-Answering Using A Deep Similarity Neural Network Neural Vector Spaces for Unsupervised Information Retrieval Task-Oriented Query Reformulation with Reinforcement Learning

In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20% in terms of recall. Ranking via Sinkhorn Propagation Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce Neural Models for Information Retrieval