论文标题
qmul-sds @ sardistance：利用网络交互来使用知识图来提高立场检测的性能
QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs
论文作者
论文摘要
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).通过进一步调查，我们最佳的实验设置从（F-AVG 0.573）提高了（F-avg 0.733）具有相同的体系结构和参数设置，并且仅结合了社交互动功能 - 突出了社交互动对模型表现的影响。
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.
