ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference (2021)
Book Chapter
Gajbhiye, A., Al Moubayed, N., & Bradley, S. (2021). ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference. In I. Farkaš, P. Masulli, S. Otte, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2021 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part V (460-472). Springer Verlag. https://doi.org/10.1007/978-3-030-86383-8_37

Neural language representation models such as BERT, pretrained on large-scale unstructured corpora lack explicit grounding to real-world commonsense knowledge and are often unable to remember facts required for reasoning and inference. Natural Langua... Read More about ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference.

Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models (2020)
Book Chapter
Gajbhiye, A., Winterbottom, T., Al Moubayed, N., & Bradley, S. (2020). Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2020 (633-646). Springer Verlag. https://doi.org/10.1007/978-3-030-61609-0_50

We consider the task of incorporating real-world commonsense knowledge into deep Natural Language Inference (NLI) models. Existing external knowledge incorporation methods are limited to lexical-level knowledge and lack generalization across NLI mode... Read More about Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models.

CAM: A Combined Attention Model for Natural Language Inference (2018)
Conference Proceeding
Gajbhiye, A., Jaf, S., Al-Moubayed, N., Bradley, S., & McGough, A. S. (2018). CAM: A Combined Attention Model for Natural Language Inference. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, …J. Saltz (Eds.), 2018 IEEE International Conference on Big Data (Big Data) ; proceedings (1009-1014). https://doi.org/10.1109/bigdata.2018.8622057

Natural Language Inference (NLI) is a fundamental step towards natural language understanding. The task aims to detect whether a premise entails or contradicts a given hypothesis. NLI contributes to a wide range of natural language understanding appl... Read More about CAM: A Combined Attention Model for Natural Language Inference.

An Exploration of Dropout with RNNs for Natural Language Inference (2018)
Conference Proceeding
Gajbhiye, A., Jaf, S., Al-Moubayed, N., McGough, A. S., & Bradley, S. (2018). An Exploration of Dropout with RNNs for Natural Language Inference. In V. Kurková, Y. Manolopoulos, B. Hammer, L. S. Iliadis, & I. G. Maglogiannis (Eds.), Artificial neural networks and machine learning - ICANN 2018 : 27th international Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, proceedings. Part III (157-167). https://doi.org/10.1007/978-3-030-01424-7_16

Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In thi... Read More about An Exploration of Dropout with RNNs for Natural Language Inference.