Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering

In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module. With our proposed model, a text is encoded to a vector representation from an word-level to a chunk-level to effectively capture the entire meaning. In particular, by adapting the hierarchical structure, our model shows very small performance degradations in longer text comprehension while other state-of-the-art recurrent neural network models suffer from it. Additionally, the latent topic clustering module extracts semantic information from target samples. This clustering module is useful for any text related tasks by allowing each data sample to find its nearest topic cluster, thus helping the neural network model analyze the entire data. We evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic domain question answering dataset, which is related to Samsung products. The proposed model shows state-of-the-art results for ranking question-answer pairs.Comment: 10 pages, Accepted as a conference paper at NAACL 201

Phase transitions for information diffusion in random clustered networks

We study the conditions for the phase transitions of information diffusion in complex networks. Using the random clustered network model, a generalisation of the Chung-Lu random network model incorporating clustering, we examine the effect of clustering under the Susceptible-Infected-Recovered (SIR) epidemic diffusion model with heterogeneous contact rates. For this purpose, we exploit the branching process to analyse information diffusion in random unclustered networks with arbitrary contact rates, and provide novel iterative algorithms for estimating the conditions and sizes of global cascades, respectively. Showing that a random clustered network can be mapped into a factor graph, which is a locally tree-like structure, we successfully extend our analysis to random clustered networks with heterogeneous contact rates. We then identify the conditions for phase transitions of information diffusion using our method. Interestingly, for various contact rates, we prove that random clustered networks with higher clustering coefficients have strictly lower phase transition points for any given degree sequence. Finally, we confirm our analytical results with numerical simulations of both synthetically-generated and real-world networks

Detecting Incongruity between News Headline and Body Text via a Deep Hierarchical Encoder

Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume. This research introduces million-scale pairs of news headline and body text dataset with incongruity label, which can uniquely be utilized for detecting news stories with misleading headlines. On this dataset, we develop two neural networks with hierarchical architectures that model a complex textual representation of news articles and measure the incongruity between the headline and the body text. We also present a data augmentation method that dramatically reduces the text input size a model handles by independently investigating each paragraph of news stories, which further boosts the performance. Our experiments and qualitative evaluations demonstrate that the proposed methods outperform existing approaches and efficiently detect news stories with misleading headlines in the real world

Internal Nasal Valve Modification via Correction of High Dorsal Deviation Using a Modified Mattress Suture Technique

High dorsal deviation of the septum can cause nasal obstruction due to internal nasal valve (INV) stenosis. We have developed a new technique using a modified mattress suture on the bony-cartilaginous junction to correct high dorsal septal deviation. This study focused on the effect of this suturing technique on the modification of the INV. We enrolled 40 patients who underwent septoplasty using a modified mattress suture technique. We retrospectively analyzed the data of the preoperative and postoperative INV angles and cross-sectional areas (CSAs), which were measured using computed tomography. In addition, we compared the patients’ subjective nasal symptoms, which were measured with the preoperative and postoperative Nasal Obstruction Symptom Evaluation (NOSE) instrument. Postoperative increases in the narrow side INV angle and CSA were achieved. Additionally, the wide side INV angle and CSA were significantly decreased postoperatively. The INV and CSA ratio (wide/narrow) were also decreased postoperatively and were brought closer to 1. The subjective nasal symptoms also exhibited significantly reduced NOSE values. In this study, we confirmed the effects of septoplasty using a modified mattress suture technique for INV modification through the comparison of the preoperative and postoperative INV angles and CSAs