Jun Xu

Publications103
h-index23
Citations2,576
Highly Influential Citations401
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Text Matching as Image Recognition
TLDR
This model, which resembles the compositional hierarchies of patterns in image recognition, can successfully identify salient signals such as n-gram and n-term matchings and demonstrates its superiority against the baselines.
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
TLDR
This work presents a new deep architecture to match two sentences with multiple positional sentence representations, generated by a bidirectional long short term memory (Bi-LSTM).
Learning Hierarchical Representation Model for NextBasket Recommendation
TLDR
This paper introduces a novel recommendation approach, namely hierarchical representation model (HRM), which can well capture both sequential behavior and users' general taste by involving transaction and user representations in prediction.
Multivariate Time Series Imputation with Generative Adversarial Networks
TLDR
Experiments show that the proposed model outperformed the baselines in terms of accuracy of imputation, and a simple model on the imputed data can achieve state-of-the-art results on the prediction tasks, demonstrating the benefits of the model in downstream applications.
A Study of MatchPyramid Models on Ad-hoc Retrieval
TLDR
The MatchPyramid models can significantly outperform several recently introduced deep matching models on the retrieval task, but still cannot compete with the traditional retrieval models, such as BM25 and language models.
SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval
TLDR
A neural learning-to-rank model called SetRank is proposed which directly learns a permutation-invariant ranking model defined on document sets of any size and Experimental results showed that the SetRank significantly outperformed the baselines include the traditional learning- to-rank models and state-of-the-art Neural IR models.
Learning to Control the Specificity in Neural Response Generation
TLDR
A novel controlled response generation mechanism to handle different utterance-response relationships in terms of specificity is proposed, which introduces an explicit specificity control variable into a sequence-to-sequence model, which interacts with the usage representation of words through a Gaussian Kernel layer.
Reinforcement Learning to Rank with Markov Decision Process
TLDR
This paper proposes a novel learning to rank model on the basis of Markov decision process (MDP), referred to as MDPRank, which Experimental results on LETOR benchmark datasets showed thatMDPRank can outperform the state-of-the-art baselines.
Reinforcing Coherence for Sequence to Sequence Model in Dialogue Generation
TLDR
Three different types of coherence models, including an unlearned similarity function, a pretrained semantic matching function, and an end-to-end dual learning architecture, are proposed in this paper, showing that the proposed models produce more specific and meaningful responses, yielding better performances against Seq2Seq models in terms of both metric-based and human evaluations.
DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval
TLDR
Experiments on both benchmark LETOR dataset and a large scale clickthrough data show that DeepRank can significantly outperform learning to ranking methods, and existing deep learning methods.
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