2.2k citations · 10.9k across the 144 of their papers we have counts for
11 papers · 1 filter
Reasoning in Vector Space: An Exploratory Study of Question Answering
Moontae Lee, Xiaodong He, Wen-tau Yih +3
Question answering tasks have shown remarkable progress with distributed vector representation. In this paper, we investigate the recently proposed Facebook bAbI tasks which consis…
Deep Reinforcement Learning with a Natural Language Action Space
Ji He, Jianshu Chen, Xiaodong He +4
This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as fou…
Stacked Attention Networks for Image Question Answering
Zichao Yang, Xiaodong He, Jianfeng Gao +2
This paper presents stacked attention networks (SANs) that learn to answer natural language questions from images. SANs use semantic representation of a question as query to search…
A Diversity-Promoting Objective Function for Neural Conversation Models
Jiwei Li, Michel Galley, Chris Brockett +2
Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.g., "I don't know") regardless of the input. W…
Recurrent Reinforcement Learning: A Hybrid Approach
Xiujun Li, Lihong Li, Jianfeng Gao +4
Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states. It is in general very challenging to construct and…
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
Jianshu Chen, Ji He, Yelong Shen +5
We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i.e., BP-sLDA), which maximizes the posterior pro…
deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets
Michel Galley, Chris Brockett, Alessandro Sordoni +6
We introduce Discriminative BLEU (deltaBLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs. Reference strings a…
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
Alessandro Sordoni, Michel Galley, Michael Auli +6
We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to add…
Joint Learning of Distributed Representations for Images and Texts
Xiaodong He, Rupesh Srivastava, Jianfeng Gao +1
This technical report provides extra details of the deep multimodal similarity model (DMSM) which was proposed in (Fang et al. 2015, arXiv:1411.4952). The model is trained via maxi…
A Deep Embedding Model for Co-occurrence Learning
Yelong Shen, Ruoming Jin, Jianshu Chen +3
Co-occurrence Data is a common and important information source in many areas, such as the word co-occurrence in the sentences, friends co-occurrence in social networks and product…
Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
Hamid Palangi, Li Deng, Yelong Shen +5
This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks with Long Short-Term Mem…