Let Me Know What to Ask: Interrogative-Word-Aware Question Generation

Question Generation (QG) is a Natural Language Processing (NLP) task that aids advances in Question Answering (QA) and conversational assistants. Existing models focus on generating a question based on a text and possibly the answer to the generated question. They need to determine the type of interrogative word to be generated while having to pay attention to the grammar and vocabulary of the question. In this work, we propose Interrogative-Word-Aware Question Generation (IWAQG), a pipelined system composed of two modules: an interrogative word classifier and a QG model. The first module predicts the interrogative word that is provided to the second module to create the question. Owing to an increased recall of deciding the interrogative words to be used for the generated questions, the proposed model achieves new state-of-the-art results on the task of QG in SQuAD, improving from 46.58 to 47.69 in BLEU-1, 17.55 to 18.53 in BLEU-4, 21.24 to 22.33 in METEOR, and from 44.53 to 46.94 in ROUGE-L.Comment: Accepted at 2nd Workshop on Machine Reading for Question Answering (MRQA), EMNLP 201

Discovering Dedicators with Topic-Based Semantic Social Networks

Influential people are known to play a key role in diffusing information in a social network. When measuring influence in a social network, most studies have focused on the use of the graph topology representing a network. As a result, popular or famous people tend to be identified as influencers. While they have a potential to influence people with the network connections by propagating information to their friends or followers, it is not clear whether they can indeed serve as an influencer as expected, especially for specific topic areas. In this paper, we introduce the notion of dedicators, which measures the extent to which a user has dedicated to transmit information in selected topic areas to the people in their egocentric networks. To detect topic-based dedicators, we propose a measure that combines both community-level and individual-level factors, which are related to the volume and the engagement level of their conversations and the degree of focus on specific topics. Having analyzed a Twitter conversation data set, we show that dedicators are not co-related with topology-based influencers; users with high in-degree influence tend to have a low dedication level while top dedicators tend to have richer conversations with others, taking advantage of smaller and manageable social networks