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Paper text:
matching is of central importance to natural language processing . in fact , many problems in natural language processing can be formalized as matching between two short - texts , with different matching relations in different applications .
for example , in paraphrase identification the relation is synonymy , and in information retrieval it is relevance . in the meantime matching is also a challenging problem , since it requires modeling of the two short - texts as well as their relation . in machine translation , for example
, the model needs to determine whether a sentence in the source language has the same meaning as a sentence in the target language . in dialogue
, the model needs to judge whether a message is an appropriate response to a given utterance .
deep neural network can model non - linear and hierarchical relations  @xcite , and thus is well suited for short - text matching in natural language processing . the very limited work in that thread ,
makes use of word embedding as the building blocks of matching model .
although embedding - based methods have been proven effective on tasks like question answering  @xcite , paraphrase identification  @xcite , and even short text conversation @xcite , they are not enough good at handling the subtlety of general short - text matching
. short - texts often represent rich content , their relations are also complicated , and more sophisticated structures are required for comparing the two short - texts .
for example , when judging the appropriateness of response  ` you should rest more . ` " to utterance  `
i have to work during the weekend ! ` " , we have to consider the semantic correspondence between  work over the weekend " and  need to rest more " , which is hard to be captured by an embedding - based model .
we study the problem of short - text matching in a general setting .
our method , named _ deep match tree _ ( deepmatch@xmath0 ) , consists of two sequentially connected components : 1 ) a mining algorithm to discover rich yet subtle patterns , defined in the product space of dependency trees , from a large corpus of paired short - texts , and 2 ) a learning algorithm to construct a deep neural network ( dnn ) for making a matching decision on the two short - texts , on the basis of the mined patterns .
the dnn model is specifically trained based on contrastive sampling of negative examples .    without loss of generality , we focus on the task of matching a response to a given tweet on weibo , a popular chinese microblog service , for which a large amount of data is available .
this is a hard problem , requiring consideration of complicated correspondence between the structures of two texts .
our experimental results show that deepmatch@xmath0 is superior to existing methods on the problem .
our main contributions are : 1 ) proposal of an algorithm for mining dependency tree matching patterns on large scale , 2 ) proposal of an algorithm for learning a deep matching model for using mined matching patterns , and 3 ) empirical validation of the efficacy and efficiency of the proposed method using large scale real datasets .    [
cols="^,^ " , ]     suppose that for we want to pick a more appropriate response from candidates and .
the mining algorithm ( algorithm 1 ) discovers the following pattern after the sameentity abstraction @xmath1 where @xmath2 stands for any named entity .
this pattern ( and its own sub - patterns ) then plays an important role in the later matching model in assigning a higher matching score to ( , ) , covering more specific patterns like @xmath3 which are filtered out in the mining step for its small support .
the proposed model is related to several threads of work in natural language processing and machine learning .    [ [ deep - matching - models ] ] deep matching models + + + + + + + + + + + + + + + + + + + +    there are other works on using deep neural networks for the matching task  @xcite , which build upon given or learned representations of objects . in our model , we try to directly mine and learn the representations of matching .
[ [ graph - based - kernel ] ] graph - based kernel + + + + + + + + + + + + + + + + + +    deepmatch@xmath0 extends the important notions in conventional graph kernels @xcite in two senses .
first , our model allows matching of two different subgraphs in two domains ( e.g. , \{`work`@xmath4 ` weekend ` } in one domain and \{`have`@xmath4 ` rest ` } in the other ) , while graph kernels only consider the common subgraphs on two sides .
second , our model captures the nonlinear and hierarchical relations between different matching patterns , while graph kernels simply add them together , with different weights determined by the types of sub - graphs .
[ [ string - rewriting - kernel ] ] string - rewriting kernel + + + + + + + + + + + + + + + + + + + + + + +    deepmatch@xmath0 is also related to the string - rewriting kernel ( srk ) @xcite for paraphrase identification , in
that srk also generates many patterns of matching and learns to weigh them in training .
the main difference is the matching patterns considered in srk are exhaustively enumerated ( although calculated in a smart way ) , while ours are discovered via a mining algorithm .
we propose a generic model for matching two short - texts , which relies on a tree - mining algorithm to discover a vast amount of matching patterns and a dnn to further perform the task using those patterns .
empirical study on the rather difficult task of tweet and response matching shows that our model can outperform competitor with large margins .
this work is supported in part by china national 973 project 2014cb340301 .
qun liu s work is partially supported by the science foundation ireland ( grant 12/ce / i2267 and 13/rc/2106 ) as part of the adapt centre at dublin city university .
many tasks in natural language processing , ranging from machine translation to question answering , can be reduced to the problem of matching two sentences or more generally two short texts . 
 we propose a new approach to the problem , called deep match tree ( deepmatch@xmath0 ) , under a general setting . 
 the approach consists of two components , 1 ) a mining algorithm to discover patterns for matching two short - texts , defined in the product space of dependency trees , and 2 ) a deep neural network for matching short texts using the mined patterns , as well as a learning algorithm to build the network having a sparse structure . 
 we test our algorithm on the problem of matching a tweet and a response in social media , a hard matching problem proposed in @xcite , and show that deepmatch@xmath0 can outperform a number of competitor models including one without using dependency trees and one based on word - embedding , all with large margins .