12,532 research outputs found

    Paraphrase Generation with Deep Reinforcement Learning

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    Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP), and plays a key role in a number of applications such as question answering, search, and dialogue. In this paper, we present a deep reinforcement learning approach to paraphrase generation. Specifically, we propose a new framework for the task, which consists of a \textit{generator} and an \textit{evaluator}, both of which are learned from data. The generator, built as a sequence-to-sequence learning model, can produce paraphrases given a sentence. The evaluator, constructed as a deep matching model, can judge whether two sentences are paraphrases of each other. The generator is first trained by deep learning and then further fine-tuned by reinforcement learning in which the reward is given by the evaluator. For the learning of the evaluator, we propose two methods based on supervised learning and inverse reinforcement learning respectively, depending on the type of available training data. Empirical study shows that the learned evaluator can guide the generator to produce more accurate paraphrases. Experimental results demonstrate the proposed models (the generators) outperform the state-of-the-art methods in paraphrase generation in both automatic evaluation and human evaluation.Comment: EMNLP 201

    Generalized Debye Sources Based EFIE Solver on Subdivision Surfaces

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    The electric field integral equation is a well known workhorse for obtaining fields scattered by a perfect electric conducting (PEC) object. As a result, the nuances and challenges of solving this equation have been examined for a while. Two recent papers motivate the effort presented in this paper. Unlike traditional work that uses equivalent currents defined on surfaces, recent research proposes a technique that results in well conditioned systems by employing generalized Debye sources (GDS) as unknowns. In a complementary effort, some of us developed a method that exploits the same representation for both the geometry (subdivision surface representations) and functions defined on the geometry, also known as isogeometric analysis (IGA). The challenge in generalizing GDS method to a discretized geometry is the complexity of the intermediate operators. However, thanks to our earlier work on subdivision surfaces, the additional smoothness of geometric representation permits discretizing these intermediate operations. In this paper, we employ both ideas to present a well conditioned GDS-EFIE. Here, the intermediate surface Laplacian is well discretized by using subdivision basis. Likewise, using subdivision basis to represent the sources, results in an efficient and accurate IGA framework. Numerous results are presented to demonstrate the efficacy of the approach

    Deep Active Learning for Dialogue Generation

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    We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on hamming-diverse beam search for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.Comment: Accepted at 6th Joint Conference on Lexical and Computational Semantics (*SEM) 2017 (Previously titled "Online Sequence-to-Sequence Active Learning for Open-Domain Dialogue Generation" on ArXiv

    A Study on the RAPD and SCAR Molecular Markers of Piper Species

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    In order to compare the genetic relationships among Kava, Pepper and it’s wild relatives and to distinguish Kava from Pepper and it’s wild relatives, we conducted research on Kava by using RAPD and SCAR molecular markers. 20 random primers selected from 80 random primers were used for RAPD amplification to identify the genetic relationships among Kava, Pepper and it’s wild relatives. Total 170 bands were amplified by 20 random primers, in which 20 bands were polymorphic (12%). Cluster analysis grouped the 28 accessions into six groups at similarity coefficient of 0.36, where 6 materials of Kava formed a group, indicating that Kava was distantly relation to Pepper and its wild relatives. Kava had 562 bp and 355 bp specific fragments amplified by primers OPQ- 02 and OPQ-03, respectively, were recycled for cloning and sequencing analysis, and then converted to SCAR markers. Two pairs of specific SCAR primers for Kava, P4.1 and P4.2, P8.1 and P8.2 were designed. PCR amplification of 28 test materials were performed using the two pairs of the specific primers respectively, the specific bands of 562 bp and 355 bp with expected sizes were amplified in 6 Kava materials but not in other materials. The results showed that primers P4.1 and P4.2, P8.1 and P8.2 might be used as specific SCAR primers for Kava germplasm resources identification. This research provided the basis for selecting rootstocks, molecular identification and the fingerprint construction of Kava
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