Frans Coenen


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Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering
Angrosh Mandya | James O’ Neill | Danushka Bollegala | Frans Coenen
Proceedings of the Twelfth Language Resources and Evaluation Conference

The Conversational Question Answering (CoQA) task involves answering a sequence of inter-related conversational questions about a contextual paragraph. Although existing approaches employ human-written ground-truth answers for answering conversational questions at test time, in a realistic scenario, the CoQA model will not have any access to ground-truth answers for the previous questions, compelling the model to rely upon its own previously predicted answers for answering the subsequent questions. In this paper, we find that compounding errors occur when using previously predicted answers at test time, significantly lowering the performance of CoQA systems. To solve this problem, we propose a sampling strategy that dynamically selects between target answers and model predictions during training, thereby closely simulating the situation at test time. Further, we analyse the severity of this phenomena as a function of the question type, conversation length and domain type.

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Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction
Angrosh Mandya | Danushka Bollegala | Frans Coenen
Proceedings of the 28th International Conference on Computational Linguistics

We propose a contextualised graph convolution network over multiple dependency-based sub-graphs for relation extraction. A novel method to construct multiple sub-graphs using words in shortest dependency path and words linked to entities in the dependency parse is proposed. Graph convolution operation is performed over the resulting multiple sub-graphs to obtain more informative features useful for relation extraction. Our experimental results show that the proposed method achieves superior performance over the existing GCN-based models achieving state-of-the-art performance on cross-sentence n-ary relation extraction dataset and SemEval 2010 Task 8 sentence-level relation extraction dataset. Our model also achieves a comparable performance to the SoTA on the TACRED dataset.


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Joint Multi-Label Attention Networks for Social Text Annotation
Hang Dong | Wei Wang | Kaizhu Huang | Frans Coenen
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We propose a novel attention network for document annotation with user-generated tags. The network is designed according to the human reading and annotation behaviour. Usually, users try to digest the title and obtain a rough idea about the topic first, and then read the content of the document. Present research shows that the title metadata could largely affect the social annotation. To better utilise this information, we design a framework that separates the title from the content of a document and apply a title-guided attention mechanism over each sentence in the content. We also propose two semantic-based loss regularisers that enforce the output of the network to conform to label semantics, i.e. similarity and subsumption. We analyse each part of the proposed system with two real-world open datasets on publication and question annotation. The integrated approach, Joint Multi-label Attention Network (JMAN), significantly outperformed the Bidirectional Gated Recurrent Unit (Bi-GRU) by around 13%-26% and the Hierarchical Attention Network (HAN) by around 4%-12% on both datasets, with around 10%-30% reduction of training time.


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A Dataset for Inter-Sentence Relation Extraction using Distant Supervision
Angrosh Mandya | Danushka Bollegala | Frans Coenen | Katie Atkinson
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)