QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs
Published in EVALITA Evaluation of NLP and Speech Tools for Italian. Proceedings of the Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian Final Workshop., 2020
Recommended citation: Alkhalifa, R., & Zubiaga, A. (2020). QMUL-SDS@ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs (short paper). In EVALITA. https://www.aaccademia.it/scheda-libro?aaref=1423
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.