In this paper, we study automatic keyphrase generation.Although conventional approaches to this task show promising results, they neglect correlation among keyphrases, resulting in duplication and coverage issues. To solve these problems, we propose a new sequence-to-sequence architecture for keyphrase generation named CorrRNN, which captures correlation among multiple keyphrases in two ways. First, we employ a coverage vector to indicate whether the word in the source document has been summarized by previous phrases to improve the coverage for keyphrases. Second, preceding phrases are taken into account to eliminate duplicate phrases and improve result coherence. Experiment results show that our model significantly outperforms the state-of-the-art method on benchmark datasets in terms of both accuracy and diversity.

With the development of deep learning (DL) and synthetic aperture radar (SAR) imaging techniques, SAR automatic target recognition has come to a breakthrough. Numerous algorithms have been proposed and competitive results have been achieved in detecting different targets. However, due to the influence of various sizes and complex background of ships, detecting multiscale ships in SAR images is still challenging. To solve the problems, a novel network, called attention receptive pyramid network (ARPN), is proposed in this article. ARPN is a two-stage detector and designed to improve the performance of detecting multiscale ships in SAR images by enhancing the relationships among nonlocal features and refining information at different feature maps. Specifically, receptive fields block (RFB) and convolutional block attention module (CBAM) are employed and combined reasonably in attention receptive block to build a top-down fine-grained feature pyramid. RFB, composed of several branches of convolutional layers with specifically asymmetric kernel sizes and various dilation rates, is used for grabbing features of ships with large aspect ratios and enhancing local features with their global dependences. CBAM, which consists of channel and spatial attention mechanisms, is utilized to boost significant information and suppress interference caused by surroundings. To evaluate the effectiveness of ARPN, experiments are conducted on SAR Ship Detection Dataset and two large-scene SAR images. The detection results illustrate that competitive performance has been achieved by our method in comparison with several CNN-based algorithms, e.g.

We study the problem of joint question answering (QA) and question generation (QG) in this paper. Our intuition is that QA and QG have intrinsic connections and these two tasks could improve each other. On one side, the QA model judges whether the generated question of a QG model is relevant to the answer. On the other side, the QG model provides the probability of generating a question given the answer, which is a useful evidence that in turn facilitates QA. In this paper we regard QA and QG as dual tasks. We propose a training framework that trains the models of QA and QG simultaneously, and explicitly leverages their probabilistic correlation to guide the training process of both models. We implement a QG model based on sequence-to-sequence learning, and a QA model based on recurrent neural network. As all the components of the QA and QG models are differentiable, all the parameters involved in these two models could be conventionally learned with back propagation. We conduct experiments on three datasets. Empirical results show that our training framework improves both QA and QG tasks. The improved QA model performs comparably with strong baseline approaches on all three datasets.

Motivation: Complex diseases are generally thought to be under the influence of one or more mutated risk genes as well as genetic and environmental factors. Many traditional methods have been developed to identify susceptibility genes assuming a single-gene disease model ('single-locus methods'). Pathway-based approaches, combined with traditional methods, consider the joint effects of genetic factor and biologic network context. With the accumulation of high-throughput SNP datasets and human biologic pathways, it becomes feasible to search for risk pathways associated with complex diseases using bioinformatics methods. By analyzing the contribution of genetic factor and biologic network context in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, we proposed an approach to prioritize risk pathways for complex diseases: Prioritizing Risk Pathways fusing SNPs and pathways (PRP). A risk-scoring (RS) measurement was used to prioritize risk biologic pathways. This could help to demonstrate the pathogenesis of complex diseases from a new perspective and provide new hypotheses. We introduced this approach to five complex diseases and found that these five diseases not only share common risk pathways, but also have their specific risk pathways, which is verified by literature retrieval. Availability: Genotype frequencies of five case-control samples were downloaded from the WTCCC online system and the address is https://www.wtccc.org.uk/info/access_to_data_samples.shtml

Recent advances cast the entity-relation extraction to a multi-turn question answering (QA) task and provide an effective solution based on the machine reading comprehension (MRC) models. However, they use a single question to characterize the meaning of entities and relations, which is intuitively not enough because of the variety of context semantics. Meanwhile, existing models enumerate all relation types to generate questions, which is inefficient and easily leads to confusing questions. In this paper, we improve the existing MRC-based entity-relation extraction model through diverse question answering. First, a diversity question answering mechanism is introduced to detect entity spans and two answering selection strategies are designed to integrate different answers. Then, we propose to predict a subset of potential relations and filter out irrelevant ones to generate questions effectively. Finally, entity and relation extractions are integrated in an end-to-end way and optimized through joint learning. Experiment results show that the proposed method significantly outperforms baseline models, which improves the relation F1 to 62.1% (+1.9%) on ACE05 and 71.9% (+3.0%) on CoNLL04. Our implementation is available at https://github.com/TanyaZhao/MRC4ERE.