737 research outputs found
Learning Semantic Representations for the Phrase Translation Model
This paper presents a novel semantic-based phrase translation model. A pair
of source and target phrases are projected into continuous-valued vector
representations in a low-dimensional latent semantic space, where their
translation score is computed by the distance between the pair in this new
space. The projection is performed by a multi-layer neural network whose
weights are learned on parallel training data. The learning is aimed to
directly optimize the quality of end-to-end machine translation results.
Experimental evaluation has been performed on two Europarl translation tasks,
English-French and German-English. The results show that the new semantic-based
phrase translation model significantly improves the performance of a
state-of-the-art phrase-based statistical machine translation sys-tem, leading
to a gain of 0.7-1.0 BLEU points
Continuous Space Models for CLIR
[EN] We present and evaluate a novel technique for learning cross-lingual continuous space models to aid cross-language information retrieval (CLIR). Our model, which is referred to as external-data composition neural network (XCNN), is based on a composition function that is implemented on top of a deep neural network that provides a distributed learning framework. Different from most existing models, which rely only on available parallel data for training, our learning framework provides a natural way to exploit monolingual data and its associated relevance metadata for learning continuous space representations of language. Cross-language extensions of the obtained models can then be trained by using a small set of parallel data. This property is very helpful for resource-poor languages, therefore, we carry out experiments on the English-Hindi language pair. On the conducted comparative evaluation, the proposed model is shown to outperform state-of-the-art continuous space models with statistically significant margin on two different tasks: parallel sentence retrieval and ad-hoc retrieval.We thank German Sanchis Trilles for helping in conducting experiments with machine translation. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce Titan GPU used for this research. The research of the first author was supported by FPI grant of UPV. The research of the third author is supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAMATER (PrometeolI/2014/030).Gupta, P.; Banchs, R.; Rosso, P. (2017). Continuous Space Models for CLIR. Information Processing & Management. 53(2):359-370. https://doi.org/10.1016/j.ipm.2016.11.002S35937053
AMC: Attention guided Multi-modal Correlation Learning for Image Search
Given a user's query, traditional image search systems rank images according
to its relevance to a single modality (e.g., image content or surrounding
text). Nowadays, an increasing number of images on the Internet are available
with associated meta data in rich modalities (e.g., titles, keywords, tags,
etc.), which can be exploited for better similarity measure with queries. In
this paper, we leverage visual and textual modalities for image search by
learning their correlation with input query. According to the intent of query,
attention mechanism can be introduced to adaptively balance the importance of
different modalities. We propose a novel Attention guided Multi-modal
Correlation (AMC) learning method which consists of a jointly learned hierarchy
of intra and inter-attention networks. Conditioned on query's intent,
intra-attention networks (i.e., visual intra-attention network and language
intra-attention network) attend on informative parts within each modality; a
multi-modal inter-attention network promotes the importance of the most
query-relevant modalities. In experiments, we evaluate AMC models on the search
logs from two real world image search engines and show a significant boost on
the ranking of user-clicked images in search results. Additionally, we extend
AMC models to caption ranking task on COCO dataset and achieve competitive
results compared with recent state-of-the-arts.Comment: CVPR 201
Consistency and Variation in Kernel Neural Ranking Model
This paper studies the consistency of the kernel-based neural ranking model
K-NRM, a recent state-of-the-art neural IR model, which is important for
reproducible research and deployment in the industry. We find that K-NRM has
low variance on relevance-based metrics across experimental trials. In spite of
this low variance in overall performance, different trials produce different
document rankings for individual queries. The main source of variance in our
experiments was found to be different latent matching patterns captured by
K-NRM. In the IR-customized word embeddings learned by K-NRM, the
query-document word pairs follow two different matching patterns that are
equally effective, but align word pairs differently in the embedding space. The
different latent matching patterns enable a simple yet effective approach to
construct ensemble rankers, which improve K-NRM's effectiveness and
generalization abilities.Comment: 4 pages, 4 figures, 2 table
Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it)
What if Information Retrieval (IR) systems did not just retrieve relevant
information that is stored in their indices, but could also "understand" it and
synthesise it into a single document? We present a preliminary study that makes
a first step towards answering this question. Given a query, we train a
Recurrent Neural Network (RNN) on existing relevant information to that query.
We then use the RNN to "deep learn" a single, synthetic, and we assume,
relevant document for that query. We design a crowdsourcing experiment to
assess how relevant the "deep learned" document is, compared to existing
relevant documents. Users are shown a query and four wordclouds (of three
existing relevant documents and our deep learned synthetic document). The
synthetic document is ranked on average most relevant of all.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieval, July 21,
2016, Pisa, Ital
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