Hierarchically-Attentive RNN for Album Summarization and Storytelling

Supplementary Material: Hierarchically-Attentive RNN for Album Summarization and Storytelling

Attentive Language Models

In this paper, we extend Recurrent Neural Network Language Models (RNN-LMs) with an attention mechanism. We show that an Attentive RNN-LM (with 14.5M parameters) achieves a better perplexity than larger RNN-LMs (with 66M parameters) and achieves performance comparable to an ensemble of 10 similar sized RNN-LMs. We also show that an Attentive RNN-LM needs less contextual information to achieve s...

Abstractive Sentence Summarization with Attentive Recurrent Neural Networks

Abstractive Sentence Summarization generates a shorter version of a given sentence while attempting to preserve its meaning. We introduce a conditional recurrent neural network (RNN) which generates a summary of an input sentence. The conditioning is provided by a novel convolutional attention-based encoder which ensures that the decoder focuses on the appropriate input words at each step of ge...

Irony Detection with Attentive Recurrent Neural Networks

Automatic Irony Detection refers to making computer understand the real intentions of human behind the ironic language. Much work has been done using classic machine learning techniques applied on various features. In contrast to sophisticated feature engineering, this paper investigates how the deep learning can be applied to the intended task with the help of word embedding. Three different d...

Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization

This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound frequency of each target vocabulary in the encoder and control the output words based on the estimation in the decoder. Our method shows significant improvement over a strong RNN-based encoder-decoder baseline and ac...