“Multi-hop Question Answering (QA) requires the machine to answer complex questions by find-ing scattering clues and reasoning from multiple documents. Graph Network (GN) and Ques-tion Decomposition (QD) are two common approaches at present. The former uses the “black-box” reasoning process to capture the potential relationship between entities and sentences, thusachieving good performance. At the same time, the latter provides a clear reasoning logical routeby decomposing multi-hop questions into simple single-hop sub-questions. In this paper, wepropose a novel method to complete multi-hop QA from the perspective of Question Genera-tion (QG). Specifically, we carefully design an end-to-end QG module on the basis of a classicalQA module, which could help the model understand the context by asking inherently logicalsub-questions, thus inheriting interpretability from the QD-based method and showing superiorperformance. Experiments on the HotpotQA dataset demonstrate that the effectiveness of ourproposed QG module, human evaluation further clarifies its interpretability quantitatively, andthorough analysis shows that the QG module could generate better sub-questions than QD meth-ods in terms of fluency, consistency, and diversity.”

“The goal of headline style transfer in this paper is to make a headline more attractive whilemaintaining its meaning. The absence of parallel training data is one of the main problems in thisfield. In this work, we design a discrete style space for unsupervised headline style transfer, shortfor D-HST. This model decomposes the style-dependent text generation into content-featureextraction and style modelling. Then, generation decoder receives input from content, style,and their mixing components. In particular, it is considered that textual style signal is moreabstract than the text itself. Therefore, we propose to model the style representation space asa discrete space, and each discrete point corresponds to a particular category of the styles thatcan be elicited by syntactic structure. Finally, we provide a new style-transfer dataset, namedas TechST, which focuses on transferring news headline into those that are more eye-catchingin technical social media. In the experiments, we develop two automatic evaluation metrics— style transfer rate (STR) and style-content trade-off (SCT) — along with a few traditionalcriteria to assess the overall effectiveness of the style transfer. In addition, the human evaluationis thoroughly conducted in terms of assessing the generation quality and creatively mimicking ascenario in which a user clicks on appealing headlines to determine the click-through rate. Ourresults indicate the D-HST achieves state-of-the-art results in these comprehensive evaluations. Introduction”