Abiola Obamuyide


2021

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Bayesian Model-Agnostic Meta-Learning with Matrix-Valued Kernels for Quality Estimation
Abiola Obamuyide | Marina Fomicheva | Lucia Specia
Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

Most current quality estimation (QE) models for machine translation are trained and evaluated in a fully supervised setting requiring significant quantities of labelled training data. However, obtaining labelled data can be both expensive and time-consuming. In addition, the test data that a deployed QE model would be exposed to may differ from its training data in significant ways. In particular, training samples are often labelled by one or a small set of annotators, whose perceptions of translation quality and needs may differ substantially from those of end-users, who will employ predictions in practice. Thus, it is desirable to be able to adapt QE models efficiently to new user data with limited supervision data. To address these challenges, we propose a Bayesian meta-learning approach for adapting QE models to the needs and preferences of each user with limited supervision. To enhance performance, we further propose an extension to a state-of-the-art Bayesian meta-learning approach which utilizes a matrix-valued kernel for Bayesian meta-learning of quality estimation. Experiments on data with varying number of users and language characteristics demonstrates that the proposed Bayesian meta-learning approach delivers improved predictive performance in both limited and full supervision settings.

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deepQuest-py: Large and Distilled Models for Quality Estimation
Fernando Alva-Manchego | Abiola Obamuyide | Amit Gajbhiye | Frédéric Blain | Marina Fomicheva | Lucia Specia
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation (QE). deepQuest-py provides access to (1) state-of-the-art models based on pre-trained Transformers for sentence-level and word-level QE; (2) light-weight and efficient sentence-level models implemented via knowledge distillation; and (3) a web interface for testing models and visualising their predictions. deepQuest-py is available at https://github.com/sheffieldnlp/deepQuest-py under a CC BY-NC-SA licence.

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Knowledge Distillation for Quality Estimation
Amit Gajbhiye | Marina Fomicheva | Fernando Alva-Manchego | Frédéric Blain | Abiola Obamuyide | Nikolaos Aletras | Lucia Specia
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Continual Quality Estimation with Online Bayesian Meta-Learning
Abiola Obamuyide | Marina Fomicheva | Lucia Specia
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Most current quality estimation (QE) models for machine translation are trained and evaluated in a static setting where training and test data are assumed to be from a fixed distribution. However, in real-life settings, the test data that a deployed QE model would be exposed to may differ from its training data. In particular, training samples are often labelled by one or a small set of annotators, whose perceptions of translation quality and needs may differ substantially from those of end-users, who will employ predictions in practice. To address this challenge, we propose an online Bayesian meta-learning framework for the continuous training of QE models that is able to adapt them to the needs of different users, while being robust to distributional shifts in training and test data. Experiments on data with varying number of users and language characteristics validate the effectiveness of the proposed approach.

2019

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Meta-Learning Improves Lifelong Relation Extraction
Abiola Obamuyide | Andreas Vlachos
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

Most existing relation extraction models assume a fixed set of relations and are unable to adapt to exploit newly available supervision data to extract new relations. In order to alleviate such problems, there is the need to develop approaches that make relation extraction models capable of continuous adaptation and learning. We investigate and present results for such an approach, based on a combination of ideas from lifelong learning and optimization-based meta-learning. We evaluate the proposed approach on two recent lifelong relation extraction benchmarks, and demonstrate that it markedly outperforms current state-of-the-art approaches.

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Model-Agnostic Meta-Learning for Relation Classification with Limited Supervision
Abiola Obamuyide | Andreas Vlachos
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper we frame the task of supervised relation classification as an instance of meta-learning. We propose a model-agnostic meta-learning protocol for training relation classifiers to achieve enhanced predictive performance in limited supervision settings. During training, we aim to not only learn good parameters for classifying relations with sufficient supervision, but also learn model parameters that can be fine-tuned to enhance predictive performance for relations with limited supervision. In experiments conducted on two relation classification datasets, we demonstrate that the proposed meta-learning approach improves the predictive performance of two state-of-the-art supervised relation classification models.

2018

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Zero-shot Relation Classification as Textual Entailment
Abiola Obamuyide | Andreas Vlachos
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

We consider the task of relation classification, and pose this task as one of textual entailment. We show that this formulation leads to several advantages, including the ability to (i) perform zero-shot relation classification by exploiting relation descriptions, (ii) utilize existing textual entailment models, and (iii) leverage readily available textual entailment datasets, to enhance the performance of relation classification systems. Our experiments show that the proposed approach achieves 20.16% and 61.32% in F1 zero-shot classification performance on two datasets, which further improved to 22.80% and 64.78% respectively with the use of conditional encoding.

2017

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The SUMMA Platform Prototype
Renars Liepins | Ulrich Germann | Guntis Barzdins | Alexandra Birch | Steve Renals | Susanne Weber | Peggy van der Kreeft | Hervé Bourlard | João Prieto | Ondřej Klejch | Peter Bell | Alexandros Lazaridis | Alfonso Mendes | Sebastian Riedel | Mariana S. C. Almeida | Pedro Balage | Shay B. Cohen | Tomasz Dwojak | Philip N. Garner | Andreas Giefer | Marcin Junczys-Dowmunt | Hina Imran | David Nogueira | Ahmed Ali | Sebastião Miranda | Andrei Popescu-Belis | Lesly Miculicich Werlen | Nikos Papasarantopoulos | Abiola Obamuyide | Clive Jones | Fahim Dalvi | Andreas Vlachos | Yang Wang | Sibo Tong | Rico Sennrich | Nikolaos Pappas | Shashi Narayan | Marco Damonte | Nadir Durrani | Sameer Khurana | Ahmed Abdelali | Hassan Sajjad | Stephan Vogel | David Sheppey | Chris Hernon | Jeff Mitchell
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.