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QMUL-SDS at SardiStance2020: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs

2020-11-02 18:17:51
Rabab Alkhalifa, Arkaitz Zubiaga
arXiv_CL
               
Abstract

This paper presents our submission to the SardiStance 2020 shared task, describing the architecture used for Task A and Task B. While our submission for Task A did not exceed the baseline, retraining our model using all the training tweets, showed promising results leading to (f-avg 0.601) using bidirectional LSTM with BERT multilingual embedding for Task A. For our submission for Task B, we ranked 6th (f-avg 0.709). With further investigation, our best experimented settings increased performance from (f-avg 0.573) to (f-avg 0.733) with same architecture and parameter settings and after only incorporating social interaction features -- highlighting the impact of social interaction on the model's performance.

Abstract (translated)

URL

https://arxiv.org/abs/2011.01181

PDF

https://arxiv.org/pdf/2011.01181.pdf

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