Dónal Landers

Also published as: Donal Landers


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Transformers and the Representation of Biomedical Background Knowledge
Oskar Wysocki | Zili Zhou | Paul O’Regan | Deborah Ferreira | Magdalena Wysocka | Dónal Landers | André Freitas
Computational Linguistics, Volume 49, Issue 1 - March 2023

Specialized transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available biomedical corpora. As such, they have the potential to encode large-scale biological knowledge. We investigate the encoding and representation of biological knowledge in these models, and its potential utility to support inference in cancer precision medicine—namely, the interpretation of the clinical significance of genomic alterations. We compare the performance of different transformer baselines; we use probing to determine the consistency of encodings for distinct entities; and we use clustering methods to compare and contrast the internal properties of the embeddings for genes, variants, drugs, and diseases. We show that these models do indeed encode biological knowledge, although some of this is lost in fine-tuning for specific tasks. Finally, we analyze how the models behave with regard to biases and imbalances in the dataset.

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SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data
Maël Jullien | Marco Valentino | Hannah Frost | Paul O’regan | Donal Landers | André Freitas
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper describes the results of SemEval 2023 task 7 – Multi-Evidence Natural Language Inference for Clinical Trial Data (NLI4CT) – consisting of 2 tasks, a Natural Language Inference (NLI) task, and an evidence selection task on clinical trial data. The proposed challenges require multi-hop biomedical and numerical reasoning, which are of significant importance to the development of systems capable of large-scale interpretation and retrieval of medical evidence, to provide personalized evidence-based care. Task 1, the entailment task, received 643 submissions from 40 participants, and Task 2, the evidence selection task, received 364 submissions from 23 participants. The tasks are challenging, with the majority of submitted systems failing to significantly outperform the majority class baseline on the entailment task, and we observe significantly better performance on the evidence selection task than on the entailment task. Increasing the number of model parameters leads to a direct increase in performance, far more significant than the effect of biomedical pre-training. Future works could explore the limitations of large models for generalization and numerical inference, and investigate methods to augment clinical datasets to allow for more rigorous testing and to facilitate fine-tuning. We envisage that the dataset, models, and results of this task will be useful to the biomedical NLI and evidence retrieval communities. The dataset, competition leaderboard, and website are publicly available.


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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.

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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.