Extracting event temporal relations is an important task for natural language understanding. Many works have been proposed for supervised event temporal relation extraction, which typically requires a large amount of human-annotated data for model training. However, the data annotation for this task is very time-consuming and challenging. To this end, we study the problem of semi-supervised event temporal relation extraction. Self-training as a widely used semi-supervised learning method can be utilized for this problem. However, it suffers from the noisy pseudo-labeling problem. In this paper, we propose the use of uncertainty-aware self-training framework (UAST) to quantify the model uncertainty for coping with pseudo-labeling errors. Specifically, UAST utilizes (1) Uncertainty Estimation module to compute the model uncertainty for pseudo-labeling unlabeled data; (2) Sample Selection with Exploration module to select informative samples based on uncertainty estimates; and (3) Uncertainty-Aware Learning module to explicitly incorporate the model uncertainty into the self-training process. Experimental results indicate that our approach significantly outperforms previous state-of-the-art methods.

Multi-sentence argument linking aims at detecting implicit event arguments across sentences, which is indispensable when textual events span across multiple sentences in a document. Previous studies suffer from the inherent limitations of error propagation and lack the explicit modeling of the local and non-local interactions in a textual event. In this paper, we propose an event-aware hierarchical encoder for multi-sentence argument linking. Specifically, we introduce a hierarchical encoder to explicitly capture the local and global interactions in a textual event. Furthermore, we introduce an auxiliary task to predict the event-relevant context in a manner of multi-task learning, which can implicitly benefit the argument linking model to be aware of the event-relevant context. The empirical results on the widely used argument linking dataset show that our model significantly outperforms the baselines, which demonstrates the effectiveness of our proposed method.

Most existing approaches to script event prediction rely on manually labeled data heavily, which is often expensive to obtain. To cope with the training data bottleneck, we investigate methods of combining multiple self-supervised tasks, i.e. tasks where models are explicitly trained with automatically generated labels. We propose two self-supervised pre-training tasks:one is End Identification and the other is Contrastive Scoring. Multi-task learning framework is then leveraged to combine these two tasks to jointly train the model. The pre-trained model is then fine-tuned using human-annotated script event prediction training data. Experimental results on the commonly used dataset show that our approach can achieve competitive performance compared to the previous models which are trained with the whole dataset by using just 10% of the training data, and our model trained on the whole dataset outperforms previous models significantly.

Community question answering (cQA) has become an important service due to the popularity of cQA archives on the web. This paper is concerned with the problem of question search. Question search in cQA aims to find the historical questions that are semantically equivalent or similar to the queried questions. In this paper, we propose a faster and better retrieval model for question search by leveraging user chosen category. After introducing the question category, we can filter certain amount of irrelevant historical questions under a wide range of leaf categories. Experimental results conducted on real cQA data set demonstrate that the proposed techniques are more effective and efficient than a variety of baseline methods.