Papers by Frans Coenen
Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering (2020.lrec-1)
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| Challenge: | Existing approaches employ human-written ground-truth answers for answering conversational questions at test time, but in a realistic scenario, the CoQA model will not have access to ground-Truth answers. |
| Approach: | They propose a sampling strategy that dynamically selects between target answers and model predictions during training, closely simulating the situation at test time. |
| Outcome: | The proposed sampling strategy closely simulates the situation at test time and significantly lowers the performance of CoQA systems. |
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction (2020.coling-main)
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| Challenge: | Existing graph convolutional networks use pruned dependency trees to filter irrelevant nodes from sentence graphs. |
| Approach: | They propose to construct multiple sub-graphs from shortest dependency path and words linked to entities in the dependency parse to obtain more informative features useful for relation extraction. |
| Outcome: | The proposed method achieves state-of-the-art performance on a sentence-level relation extraction dataset and the SemEval 2010 Task 8 sentence- level relation extraction data. |
Joint Multi-Label Attention Networks for Social Text Annotation (N19-1)
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| Challenge: | Present research shows that title metadata could affect social annotation. |
| Approach: | They propose a title-guided attention network for document annotation with user-generated tags that separates the title from the content of a document and applies a semantic-based loss regulariser over each sentence in the content. |
| Outcome: | The proposed approach outperforms the Bi-GRU and Hierarchical Attention Network (HAN) on two open datasets with 10%-30% reduction in training time. |
A Dataset for Inter-Sentence Relation Extraction using Distant Supervision (L18-1)
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| Challenge: | Existing methods for intra-sentence relation extraction use a distance supervision method to extract relations between entities. |
| Approach: | They propose a benchmark dataset for the task of inter-sentence relation extraction using relations previously used for intra-sentent relation extraction. |
| Outcome: | The proposed dataset is compared with baseline models and recurrent neural network models on the developed dataset. |