This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A). Subtask A addresses the challenge of rumour stance classification, which involves identifying the attitude of Twitter users towards the truthfulness of the rumour they are discussing. Stance classification is considered to be an important step towards rumour verification, therefore performing well in this task is expected to be useful in debunking false rumours. In this work we classify a set of Twitter posts discussing rumours into either supporting, denying, questioning or commenting on the underlying rumours. We propose a LSTM-based sequential model that, through modelling the conversational structure of tweets, which achieves an accuracy of 0.784 on the RumourEval test set outperforming all other systems in Subtask A.
A model based on bidirectional LSTM encoding of tweets conditioned on targets has been shown to achieve state-of-the-art on the SemEval-2016 task 6 dataset @cite_15 . However the RumourEval task is different as it addresses conversation threads. and consider the sequential nature of tweet threads in their works. employ Hawkes processes to classify temporal sequences of tweets. They show the importance of using both the textual content and temporal information about the tweets, disregarding the discourse structure. model the conversational structure of source tweets and subsequent replies: as a linear chain and as a tree. They use linear- and tree- versions of a CRF classifier, outperforming the approach by .
@cite_15
2017187090
We describe a probabilistic approach to content selection for meeting summarization. We use skipchain Conditional Random Fields (CRF) to model non-local pragmatic dependencies between paired utterances such as Question-Answer that typically appear together in summaries, and show that these models outperform linear-chain CRFs and Bayesian models in the task. We also discuss different approaches for ranking all utterances in a sequence using CRFs. Our best performing system achieves 91.3 of human performance when evaluated with the Pyramid evaluation metric, which represents a 3.9 absolute increase compared to our most competitive non-sequential classifier.