Zero-shot language transfer for cross-lingual sentence retrieval using bidirectional attention model

We present a neural architecture for cross-lingual mate sentence retrieval which encodes sentences in a joint multilingual space and learns to distinguish true translation pairs from semantically related sentences across languages. The proposed model combines a recurrent sequence encoder with a bidirectional attention layer and an intra-sentence attention mechanism. This way the final fixed-size sentence representations in each training sentence pair depend on the selection of contextualized token representations from the other sentence. The representations of both sentences are then combined using the bilinear product function to predict the relevance score. We show that, coupled with a shared multilingual word embedding space, the proposed model strongly outperforms unsupervised cross-lingual ranking functions, and that further boosts can be achieved by combining the two approaches. Most importantly, we demonstrate the model's effectiveness in zero-shot language transfer settings: our multilingual framework boosts cross-lingual sentence retrieval performance for unseen language pairs without any training examples. This enables robust cross-lingual sentence retrieval also for pairs of resource-lean languages, without any parallel data

Relevance Measures Using Geographic Scopes and Types

This paper proposes two kinds of relevance measures to rank documents by geographic restriction: scope-based and type-based. The non-geographic and geographic relevance scores are combined using a weighted harmonic mean. The proposed relevance measures and weighting schemes are evaluated on GeoCLEF 2007 dataset with encouraging performance over the standard IR performance. The best performance is achieved when the importance of non-geographic relevance scores outweigh the importance of geographic relevance scores

Content-Based Image Retrieval Using Combined 2D Attribute Pattern Spectra

This work proposes a region-based shape signature that uses a combination of three different types of pattern spectra. The proposed method is inspired by the connected shape filter proposed by Urbach et al. We extract pattern spectra from the red, green and blue color bands of an image then incorporate machine learning techniques for application in photographic image retrieval. Our experiments show that the combined pattern spectrum gives an improvement of approximately 30% in terms of mean average precision and precision at 20 with respect to Urbach et al’s method

Reranking Hypotheses of Machine-Translated Queries for Cross-Lingual Information Retrieval

Machine Translation (MT) systems employed to translate queries for Cross-Lingual Information Retrieval typically produce single translation with maximum translation quality. This, however, might not be optimal with respect to retrieval quality and other translation variants might lead to better retrieval results. In this paper, we explore a method exploiting multiple translations produced by an MT system, which are reranked using a supervised machine-learning method trained to directly optimize the retrieval quality. We experiment with various types of features and the results obtained on the medical-domain test collection from the CLEF eHealth Lab series show significant improvement of retrieval quality compared to a system using single translation provided by MT

Extracting Bimodal Representations for Language-Based Image Retrieval

This paper explores two approaches to multimedia indexing that might contribute to the advancement of text-based conceptual search for pictorial information. Insights from relatively mature retrieval areas (spoken document retrieval and cross-language retrieval) are taken as a starting point for an investigation of the usefulness of the concept of bimodal dictionaries and of clustering features from multi-modal documents into one semantic space. One of the advantages of the presented techniques is that they are domain independent. 1 Introduction Among the various types of objects that one could want to search for in the multimedia domain, image content seems to be one of the more challenging types. Speedy and easy access to image content in the general domain is not supported by today's search tools and technology, and in spite of progress in content based image retrieval or advances in the area of video logging (a technique which reuses subtitles or speech transcripts for the..

Extracting bimodal representations for language-based image text retrieval

This paper explores two approaches to multimedia indexing that might contribute to the advancement of text-based conceptual search for pictorial information. Insights from relatively mature retrieval areas (spoken document retrieval and cross-language retrieval) are taken as a starting point for an investigation of the usefulness of the concept of bimodal dictionaries and of clustering features from multi-modal documents into one semantic space. One of the advantages of the presented techniques is that they are domain independent