Hybrid embedding and joint training of stacked encoder for opinion question machine reading comprehension

Opinion question machine reading comprehension(MRC)requires a machine to answer questions by analyzing corresponding passages.Compared with traditional MRC tasks where the answer to every question is a segment of text in corresponding passages,opinion question MRC is more challenging because the answer to an opinion question may not appear in corresponding passages but needs to be deduced from multiple sentences.In this study,a novel framework based on neural networks is proposed to address such problems,in which a new hybrid embedding training method combining text features is used.Furthermore,extra attention and output layers which generate auxiliary losses are introduced to jointly train the stacked recurrent neural networks.To deal with imbalance of the dataset,irrelevancy of question and passage is used for data augmentation.Experimental results show that the proposed method achieves state-of-the-art performance.We are the biweekly champion in the opinion question MRC task in Artificial Intelligence Challenger 2018(AIC2018).

Disambiguating named entities with deep supervised learning via crowd labels

Named entity disambiguation （NED） is the task of linking mentions of ambiguous entities to their referenced entities in a knowledge base such as Wikipedia. We propose an approach to effectively disentangle the discriminative features in the manner of collaborative utilization of collective wisdom （via human-labeled crowd labels） and deep learning （via human-generated data） for the NED task. In particular, we devise a crowd model to elicit the underlying features （crowd features） from crowd labels that indicate a matching candidate for each mention, and then use the crowd features to fine-tune a dynamic convolutional neural network （DCNN）. The learned DCNN is employed to obtain deep crowd features to enhance traditional hand-crafted features for the NED task. The proposed method substantially benefits from the utilization of crowd knowledge （via crowd labels） into a generic deep learning for the NED task. Experimental analysis demonstrates that the proposed approach is superior to the traditional hand-crafted features when enough crowd labels are gathered.

Temporality-enhanced knowledge memory network for factoid question answering

Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language.How to efficiently identify the exact answer with respect to a given question has become an active line of research.Previous approaches in factoid question answering tasks typically focus on modeling the semantic relevance or syntactic relationship between a given question and its corresponding answer.Most of these models suffer when a question contains very little content that is indicative of the answer.In this paper,we devise an architecture named the temporality-enhanced knowledge memory network(TE-KMN) and apply the model to a factoid question answering dataset from a trivia competition called quiz bowl.Unlike most of the existing approaches,our model encodes not only the content of questions and answers,but also the temporal cues in a sequence of ordered sentences which gradually remark the answer.Moreover,our model collaboratively uses external knowledge for a better understanding of a given question.The experimental results demonstrate that our method achieves better performance than several state-of-the-art methods.