Distraction-Based Neural Networks for Modeling Document

By Qian Chen, Xiao-Dan Zhu, Zhenhua Ling, Si Wei, Hui Jiang. 2016

88 citations recorded by the source corpus.

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Citations in the corpus

A Deep Reinforced Model for Abstractive Summarization (2017, methodology): “Chen et al. (2016) applied different kinds of attention mechanisms for summarization on the CNN dataset, and Nallapati et al. (2016) used different attention and pointer functions on the CNN and Daily Mail datasets combined.”
Get To The Point: Summarization with Pointer-Generator Networks (2017, methodology): “In this respect our approach is similar to Xu et al. (2015), who apply a coverage-like method to image cap-tioning, and Chen et al. (2016), who also incorporate a coverage mechanism (which they call ‘dis-traction’) as described in equation (11) into neural summarization of longer text.”
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (2018, background): “Unlike most existing abstractive approaches (Rush et al., 2015; Chen et al., 2016; Nallapati et al., 2016; See et al., 2017; Tan and Wan, 2017; Paulus et al., 2018; Pasunuru and Bansal, 2018; Celikyilmaz et al., 2018) which rely on an encoder-decoder architecture modeled by recurrent neural networks”
Enhanced LSTM for Natural Language Inference (2016, background): “…based models have been shown to be effective in a wide range of tasks, including machine translation (Bahdanau et al., 2014), speech recognition (Chorowski et al., 2015; Chan et al., 2016), image caption (Xu et al., 2015), and text summarization (Rush et al., 2015; Chen et al., 2016), among others.”
Multi-Reward Reinforced Summarization with Saliency and Entailment (2018, background): “…et al., 2015; Dohare and Karnick, 2017), and neural network models with pointer-copy mechanism and coverage (Rush et al., 2015; Chopra et al., 2016; Chen et al., 2016; Nallapati et al., 2016; See et al., 2017), as well as reinforce-based metric rewards (Ranzato et al., 2015; Paulus et al., 2017).”
Ranking Sentences for Extractive Summarization with Reinforcement Learning (2018, methodology): “We also compared our model against the abstractive systems of Chen et al. (2016), Nallapati et al. (2016), See et al. (2017), and Tan and Wan (2017).”
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting (2018, background): “Some of the recent success in neural abstractive models include hierarchical attention (Nallapati et al., 2016), coverage (Suzuki and Nagata, 2016; Chen et al., 2016; See et al., 2017), RL based metric optimization (Paulus et al., 2018), graph-based attention (Tan et al., 2017), and the copy…”
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (2018, methodology): “Recent work has also adopted machine translation inspired neural seq2seq models for abstractive summarization with advances in hierarchical, distractive, saliency, and graph-attention modeling (Rush et al., 2015; Chopra et al., 2016; Nallapati et al., 2016; Chen et al., 2016; Tan et al., 2017).”
Neural Extractive Summarization with Side Information (2017, background): “compare our results to the extractive system of Nallapati, Zhai, and Zhou (2017) because they report their results on the DailyMail dataset and their code is not available. The abstractive systems of Chen et al. (2016) and Tan and Wan (2017) report their results on the CNN dataset, however, their re”
Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference (2016, background): “…based models have been shown to be effective in a wide range of tasks, including machine translation (Bahdanau et al., 2014), speech recognition (Chorowski et al., 2015; Chan et al., 2016), image caption (Xu et al., 2015), and text summarization (Rush et al., 2015; Chen et al., 2016), among others.”
Enhancing Sentence Embedding with Generalized Pooling (2018, methodology): “nt set for MultiNLI) to select models for testing. To help replicate our results, we publish our code3, which is developed from our codebase for multiple tasks (Chen et al., 2018; Chen et al., 2017a; Chen et al., 2016; Zhang et al., 2017). Speciﬁcally, we use Adam (Kingma and Ba, 2014) for optimizat”
Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference (2017, methodology): “Among them, Enhanced Sequential Inference Model (ESIM) with cross-sentence attention represents the state of the art (Chen et al., 2016b).”
Natural Language Inference with External Knowledge (2017, background): “In general, external knowledge have been shown to be effective in a wide range of NLP tasks, including machine translation (Shi et al., 2016; Zhang et al., 2017), language modeling (Ahn et al., 2016), and dialogue system (Chen et al., 2016).”
Neural Natural Language Inference Models Enhanced with External Knowledge (2017, methodology): “…for other NLP tasks, including word embedding (Chen et al., 2015; Faruqui et al., 2015; Liu et al., 2015; Wieting et al., 2015; Mrksic et al., 2017), machine translation (Shi et al., 2016; Zhang et al., 2017b), language modeling (Ahn et al., 2016), and dialogue systems (Chen et al., 2016b).”