Dónal Landers

Also published as: Donal Landers


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What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP
Oskar Wysocki | Malina Florea | Dónal Landers | André Freitas
Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems

SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the contributions behind SemEval. By understanding the distribution of task types, metrics, architectures, participation and citations over time we aim to answer the question on what is being evaluated by SemEval.

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Encoding Explanatory Knowledge for Zero-shot Science Question Answering
Zili Zhou | Marco Valentino | Donal Landers | André Freitas
Proceedings of the 14th International Conference on Computational Semantics (IWCS)

This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge Transfer), a novel method for the automatic transfer of explanatory knowledge through neural encoding mechanisms. We demonstrate that N-XKT is able to improve accuracy and generalization on science Question Answering (QA). Specifically, by leveraging facts from background explanatory knowledge corpora, the N-XKT model shows a clear improvement on zero-shot QA. Furthermore, we show that N-XKT can be fine-tuned on a target QA dataset, enabling faster convergence and more accurate results. A systematic analysis is conducted to quantitatively analyze the performance of the N-XKT model and the impact of different categories of knowledge on the zero-shot generalization task.