4 research outputs found
Towards meta-interpretive learning of programming language semantics
We introduce a new application for inductive logic programming: learning the
semantics of programming languages from example evaluations. In this short
paper, we explored a simplified task in this domain using the Metagol
meta-interpretive learning system. We highlighted the challenging aspects of
this scenario, including abstracting over function symbols, nonterminating
examples, and learning non-observed predicates, and proposed extensions to
Metagol helpful for overcoming these challenges, which may prove useful in
other domains.Comment: ILP 2019, to appea
A Logic-based Multi-agent System for Ethical Monitoring and Evaluation of Dialogues
In Proceedings ICLP 2021, arXiv:2109.0791
Tune your brown clustering, please
Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal