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In this paper, we propose a method that exploits full parsing information by repre- senting it as features of argument classifi- cation models and as constraints in integer linear learning …...

In this paper, we give a description of the ma- chine translation system developed at DCU that was used for our participation in the eval- uation campaign of the Third …...

Weights of the various components in a standard Statistical Machine Translation model are usually estimated via Minimum Error Rate Training. With this, one finds their optimum value on a development …...

This paper describes the SCHWA system entered by the University of Sydney in Se- mEval 2010 Task 12 – Parser Evaluation using Textual Entailments (Yuret et al., 2010). Our system …...

AbstractWe present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018. It is a simple neural network consisting of three parts, collectively judging whether the logic …...

AbstractIn this paper, we present the system submitted to “SemEval-2020 Task 12”. The proposed system aims at automatically identify the Offensive Language in Arabic Tweets. A machine learning based approach …...

AbstractThis paper explains our teams’ submission to the Shared Task of Fine-Grained Propaganda Detection in which we propose a sequential BERT-CRF based Span Identification model where the fine-grained detection is …...

AbstractThis paper presents six document classification models using the latest transformer encoders and a high-performing ensemble model for a task of offensive language identification in social media. For the individual …...

AbstractStandard train-dev-test splits used to benchmark multiple models against each other are ubiquitously used in Natural Language Processing (NLP). In this setup, the train data is used for training the …...