Researchers have been paying increasing attention to rumour evaluation due to the rapid spread of unsubstantiated rumours on social media platforms, including SemEval 2019 task 7. However, labelled data for learning rumour veracity is scarce, and labels in rumour stance data are highly disproportionate, making it challenging for a model to perform supervised-learning adequately. We propose an inference chain-based system, which fully utilizes conversation structure-based knowledge in the limited data and expand the training data in minority categories to alleviate class imbalance. Our approach obtains 12.6% improvement upon the baseline system for subtask A, ranks 1st among 21 systems in subtask A, and ranks 4th among 12 systems in subtask B.
We introduce a new task named Story Ending Generation (SEG), which aims at generating a coherent story ending from a sequence of story plot. We propose a framework consisting of a Generator and a Reward Manager for this task. The Generator follows the pointer-generator network with coverage mechanism to deal with out-of-vocabulary (OOV) and repetitive words. Moreover, a mixed loss method is introduced to enable the Generator to produce story endings of high semantic relevance with story plots. In the Reward Manager, the reward is computed to fine-tune the Generator with policy-gradient reinforcement learning (PGRL). We conduct experiments on the recently-introduced ROCStories Corpus. We evaluate our model in both automatic evaluation and human evaluation. Experimental results show that our model exceeds the sequence-to-sequence baseline model by 15.75% and 13.57% in terms of CIDEr and consistency score respectively.
Since the resources of Community Question Answering are abundant and information sharing becomes universal, it will be increasingly difficult to find factual information for questioners in massive messages. SemEval 2019 task 8 is focusing on these issues. We participate in the task and use Generative Pretrained Transformer (OpenAI GPT) as our system. Our innovations are data extension, feature extraction, and input transformation. For contextual knowledge enhancement, we extend the training set of subtask A, use several features to improve the results of our system and adapt the input formats to be more suitable for this task. We demonstrate the effectiveness of our approaches, which achieves 81.95% of subtask A and 61.08% of subtask B in accuracy on the SemEval 2019 task 8.
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