We propose a dialogue state tracker based on long short term memory (LSTM) neural networks. LSTM is an extension of a recurrent neural network (RNN), which can better consider distant dependencies in sequential input. We construct a LSTM network that receives utterances of dialogue participants as input, and outputs the dialogue state of the current utterance. The input utterances are separated...

We aim to learn hypernymy present in distributed word representations using a deep LSTM neural network. We hypothesize that the semantic information of hypernymy is distributed differently across the components of the hyponym and hypernym vectors for varying examples of hypernymy. We use an LSTM cell with a replacement gate to adjust the state of the network as different examples of hypernymy a...

In this paper we implemented different models to solve the review usefulness classification problem. Both feed-forward neural network and LSTM were able to beat the baseline model. Performances of the models are evaluated using 0-1 loss and F-1 scores. In general, LSTM outperformed feed-forward neural network, as we trained our own word vectors in that model, and LSTM itself was able to store m...

In this paper, we explore the possibility of leveraging Residual Networks (ResNet), a powerful structure in constructing extremely deep neural network for image understanding, to improve recurrent neural networks (RNN) for modeling sequential data. We show that for sequence classification tasks, incorporating residual connections into recurrent structures yields similar accuracy to Long Short T...

Video caption refers to generating a descriptive sentence for a specific short video clip automatically, which has achieved remarkable success recently. However, most of the existing methods focus more on visual information while ignoring the synchronized audio cues. We propose three multimodal deep fusion strategies to maximize the benefits of visual-audio resonance information. The first one ...