Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

In information retrieval, learning to rank constructs a machine-based ranking model which given a query, sorts the search results by their degree of relevance or importance to the query.
Neural networks have been successfully applied to this problem, and in this paper, we propose an attention-based deep neural network which better incorporates different embeddings of the queries and search results with an attention-based mechanism.
This model also applies a decoder mechanism to learn the ranks of the search results in a listwise fashion.
The embeddings are trained with convolutional neural networks or the word2vec model.
We demonstrate the performance of this model with image retrieval and text querying data sets.
Learning to rank applies supervised or semi-supervised machine learning to construct ranking models for information retrieval problems.
In learning to rank, a query is given and a number of search results are to be ranked by their relevant importance given the query.
Many problems in information retrieval can be formulated or partially solved by learning to rank.
In learning to rank, there are typically three approaches: the pointwise, pairwise, and listwise approaches Liu (2011) .
The pointwise approach assigns an importance score to each pair of query and search result.
The pairwise approach discerns which search result is more relevant for a certain query and a pair of search results.
The listwise approach outputs the ranks for all search results given a specific query, therefore being the most general.
For learning to rank, neural networks are known to enjoy a success.
Generally in such models, neural networks are applied to model the ranking probabilities with the features of queries and search results as the input.
For instance, RankNet Burges et al. (2005) applies a neural network to calculate a probability for any search result being more relevant compared to another.
Each pair of query and search result is combined into a feature vector, which is the input of the neural network, and a ranking priority score is the output.
Another approach learns the matching mechanism between the query and the search result, which is particularly suitable for image retrieval.
Usually the mechanism is represented by a similarity matrix which outputs a bilinear form as the ranking priority score; for instance, such a structure is applied in Severyn & Moschitti (2015) .
We postulate that it could be beneficial to apply multiple embeddings of the queries and search results to a learning to rank model.
It has already been observed that for training images, applying a committee of convolutional neural nets improves digit and character recognition .
From such an approach, the randomness of the architecture of a single neural network can be effectively reduced.
For training text data, combining different techniques such as tf-idf, latent Dirichlet allocation (LDA) Blei et al. (2003) , or word2vec Mikolov et al. (2013) , has also been explored by Das et al. (2015) .
This is due to the fact that it is relatively hard to judge different models a priori.
However, we have seen no literature on designing a mechanism to incorporate different embeddings for ranking.
We hypothesize that applying multiple embeddings to a ranking neural network can improve the accuracy not only in terms of "averaging out" the error, but it can also provide a more robust solution compared to applying a single embedding.
For learning to rank, we propose the application of the attention mechanism Bahdanau et al. (2015) ; , which is demonstrated to be successful in focusing on different aspects of the input so that it can incorporate distinct features.
It incorporates different embeddings with weights changing over time, derived from a recurrent neural network (RNN) structure.
Thus, it can help us better summarize information from the query and search results.
We also apply a decoder mechanism to rank all the search results, which provides a flexible list-wise ranking approach that can be applied to both image retrieval and text querying.
Our model has the following contributions: (1) it applies the attention mechanism to listwise learning to rank problems, which we think is novel in the learning to rank literature; (2) it takes different embeddings of queries and search results into account, incorporating them with the attention mechanism; (3) double attention mechanisms are applied to both queries and search results.
Section 2 reviews RankNet, similarity matching, and the attention mechanism in details.
Section 3 constructs the attention-based deep net for ranking, and discusses how to calibrate the model.
Section 4 demonstrates the performance of our model on image retrieval and text querying data sets.
Section 5 discusses about potential future research and concludes the paper.
Both queries and search results can be embedded with neural networks.
Given an input vector x 0 representing a query or a search result, we denote the l-th layer in a neural net as
is the bias, and f is a nonlinear activation function.
If the goal is classification with C categories, then (P (y = 1), . . . ,
, where y is a class indicator, and sof tmax(u)
From training this model, we may take the softmax probabilities as the embedding, and create different embeddings with different neural network structures.
For images, convolutional neural nets (CNNs) LeCun et al. (1998) are more suitable, in which each node only takes information from neighborhoods of the previous layer.
Pooling over each neighborhood is also performed for each layer of a convolutional neural net.
With different networks, we can obtain different embeddings c 1 , . . . , c M .
In the attention mechanism below, we generate the weights α t with an RNN structure, and summarize c t in a decoder series z t ,
Here f AT T and φ θ are chosen as tanh layers in our experiments.
Note that the attention weight α t at state t depends on the previous attention weight α t−1 , the embeddings, and the previous decoder state z t−1 , and the decoder series z t sums up information of c t up to state t.
As aforementioned, given multiple embeddings, the ranking process can be viewed as applying different attention weights to the embeddings and generating the decoder series z t , offering a listwise approach.
However, since there are features for both queries and search results, we consider them as separately, and apply double attention mechanisms to each of them.
Our full model is described below.
In this paper, we proposed a new neural network for learning-to-rank problems which applies the attention mechanism to incorporate different embeddings of queries and search results, and ranks the search results with a listwise approach.
Data experiments show that our model yields improvements over state-of-the-art techniques.
For the future, it would be of interest to consider improving the RNN structure in the attention mechanism, and tailoring the embedding part of the neural network to this problem.
P. Wu, S. Hoi, H. Xia, P. Zhao, D. Wang, and C. Miao.
Online multimodal deep similarity learning with application to image retrieval.
In Proceedings of the 21st ACM international conference on Multimedia, Barcelona, Spain, 2013.
learning to rank with several embeddings and attentions