Constituency Tree Representation for Argument Unit Recognition

Abstract

The extraction of arguments from sentences is usually studied by considering only the neighbourhood dependencies of words. Such a representation does not rely on the syntactic structure of the sentence and can lead to poor results especially in languages where grammatical categories are scattered in the sentence. In this paper, we investigate the advantages of using a constituency tree representation of sentences for argument discourse unit (ADU) prediction. We demonstrate that the constituency structure is more powerful than simple linear dependencies between neighbouring words in the sentence. Our work was organised as follows: First, we compare the maximum depth allowed for our constituency trees. This first step allows us to choose an optimal maximum depth. Secondly, we combine this structure with graph neural networks, which are very successful in neural network tasks. Finally, we evaluate the benefits of adding a conditional random field to model global dependencies between labels, given local dependency rules. We improve the current best models for argument unit recognition at token level and also present an explainability method to evaluate the suitability of our model architecture.

Tasks

Reproductions