Lingua-Align: An Experimental Toolbox for Automatic Tree-to-Tree Alignment

Abstract

In this paper we present an experimental toolbox for automatic tree-to-tree alignment based on a binary classification model. The aligner implements a recurrent architecture for structural prediction using history features and a sequential classification procedure. The discriminative base classifier uses a log-linear model in the current setup which enables simple integration of various features extracted from the data. The Lingua-Align toolbox provides a flexible framework for feature extraction including contextual properties and implements several alignment inference procedures. Various settings and constraints can be controlled via a simple frontend or called from external scripts. Lingua-Align supports different treebank formats and includes additional tools for conversion and evaluation. In our experiments we can show that our tree aligner produces results with high quality and outperforms unsupervised techniques proposed otherwise. It also integrates well with another existing tool for manual tree alignment which makes it possible to quickly integrate additional training material and to run semi-automatic alignment strategies.

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