Introduction

This paper looks into an advancement in Sentiment Analysis, the task of determining whether a phrase has a positive or negative connotation / meaning. More formally, sentiment can be defined as “a view or attitude toward a situation or event”. At the time, LSTMs were the most commonly used units in sentiment analysis networks. Authored by Kai Sheng Tai, Richard Socher, and Christopher Manning, this paper introduces a novel way of chaining together LSTMs in a non-linear structure.

The motivation behind this non-linear arrangement lies in the notion that natural language exhibits the property that words in sequence become phrases. These phrases, depending on the order of the words, can hold different meanings from their original word components. In order to represent this characteristic, a network of LSTM units must be arranged into a tree structure where different units are affected by their children nodes.

Network Architecture

One of the differences between a Tree-LSTM and a standard one is that the hidden state of the latter is a function of the current input and the hidden state at the previous time step. However, with a Tree-LSTM, its hidden state is a function of the current input and the hidden states of its child units.

With this new tree-based structure, there are some mathematical changes including child units having forget gates. For those interested in the details, check the paper for more info. What I would like to focus on, however, is understanding why these models work better than a linear LSTM.

With a Tree-LSTM, a single unit is able to incorporate the hidden states of all of its children nodes. This is interesting because a unit is able to value each of its children nodes differently. During training, the network could realize that a specific word (maybe the word “not” or “very” in sentiment analysis) is extremely important to the overall sentiment of the sentence. The ability to value that node higher provides a lot of flexibility to network and could improve performance.