Although Neural Machine Translation (NMT) models have advanced state-of-the-art performance in machine translation, they face problems like the inadequate translation. We attribute this to that the standard Maximum Likelihood Estimation (MLE) cannot judge the real translation quality due to its several limitations. In this work, we propose an adequacy-oriented learning mechanism for NMT by casting translation as a stochastic policy in Reinforcement Learning (RL), where the reward is estimated by explicitly measuring translation adequacy. Benefiting from the sequence-level training of RL strategy and a more accurate reward designed specifically for translation, our model outperforms multiple strong baselines, including (1) standard and coverage-augmented attention models with MLE-based training, and (2) advanced reinforcement and adversarial training strategies with rewards based on both word-level BLEU and character-level chrF3. Quantitative and qualitative analyses on different language pairs and NMT architectures demonstrate the effectiveness and universality of the proposed approach.
'' denotes discriminator and O '' denotes orientator. MRT '' indicates minimum risk training @cite_22 , and D @math '' indicates adversarial training with a CNN-based discriminator @cite_15 . # Para.'' denotes the number of parameters, and Speed'' denotes the training speed (words second). @math '' and @math '' indicate statistically significant difference ( @math and @math respectively) from the corresponding baseline.
@cite_15 @cite_22
2607987856 2195405088
This paper describes our system designed for the NLPCC 2015 shared task on Chinese word segmentation WS and POS tagging for Weibo Text. We treat WS and POS tagging as two separate tasks and use a cascaded approach. Our major focus is how to effectively exploit multiple heterogeneous data to boost performance of statistical models. This work considers three sets of heterogeneous data, i.e., Weibo @math , 10K sentences, Penn Chinese Treebank 7.0 @math , 50K, and People's Daily @math , 280K. For WS, we adopt the recently proposed coupled sequence labeling to combine @math , @math , and @math , boosting F1 score from @math baseline model trained on only @math to @math @math . For POS tagging, we adopt an ensemble approach combining coupled sequence labeling and the guide-feature based method, since the three datasets have three different annotation standards. First, we convert @math into the annotation style of @math based on coupled sequence labeling, denoted by @math . Then, we merge CTB7 and @math to train a POS tagger, denoted by @math , which is further used to produce guide features on @math . Finally, the tagging F1 score is improved from 87.93 to 88.99 +1.06 . Let @math denote the cover time of a two dimensional compact manifold @math by a Wiener sausage of radius @math . We prove that @math is tight, where @math denotes the area of @math . We study packing linear programs (LPs) in an online model where the columns are presented to the algorithm in random order. This natural problem was investigated in various recent studies motivated, e.g., by online ad allocations and yield management, where rows correspond to resources and columns to requests specifying demands for resources. Our main contribution is a @math -competitive online algorithm. Here @math denotes the column sparsity, i.e., the maximum number of resources that occur in a single column, and @math denotes the capacity ratio @math , i.e., the ratio between the capacity of a resource and the maximum demand for this resource. In other words, we achieve a @math -approximation if the capacity ratio satisfies @math , which is known to be the best possible for any (randomized) online algorithms. Our result improves exponentially on previous work with respect to the capacity ratio. In contrast to existing results on packing LP ... We give sufficient conditions on a symmetric tensor @math to satisfy the following equality: the symmetric rank of @math , denoted as @math , is equal to the rank of @math , denoted as @math . This is done by considering the rank of the unfolded @math viewed as a matrix @math . The condition is @math . In particular, @math for @math for the cases @math . We discuss the analogues of the above results for border rank and best approximations of symmetric tensors. The @math -core of a digraph is the largest sub-digraph with minimum in-degree and minimum out-degree at least @math and @math respectively. For @math , we establish existence of the threshold edge-density @math , such that the random digraph @math , on the vertex set @math with @math edges, asymptotically almost surely has a giant @math -core if @math , and has no @math -core if @math . Specifically, denoting @math by @math , we prove that @math . We consider @math Hermitian matrices with i.i.d. entries @math whose tail probabilities @math behave like @math for some @math and @math . We establish a large deviation principle for the empirical spectral measure of @math with speed @math with a good rate function @math that is finite only if @math is of the form @math for some probability measure @math on @math , where @math denotes the free convolution and @math is Wigner's semicircle law. We obtain explicit expressions for @math in terms of the @math th moment of @math . The proof is based on the analysis of large deviations for the empirical distribution of very sparse random rooted networks. Let @math denote the first passage time to 1 of a standard Brownian motion. It is well known that as @math goes to infinity, @math goes to zero at rate @math , where @math equals @math . The goal of this note is to establish a quantitative, infinite dimensional version of this result. Namely, we will prove the existence of positive and finite constants @math and @math , such that for all @math , @math where @math ' denotes the natural logarithm, and @math is the Fukushima-Malliavin capacity on the space of continuous functions. We study undirected networks with edge costs that satisfy the triangle inequality. Let @math denote the number of nodes. We present an @math -approximation algorithm for a generalization of the metric-cost subset @math -node-connectivity problem. Our approximation guarantee is proved via lower bounds that apply to the simple edge-connectivity version of the problem, where the requirements are for edge-disjoint paths rather than for openly node-disjoint paths. A corollary is that, for metric costs and for each @math , there exists a @math -node connected graph whose cost is within a factor of @math of the cost of any simple @math -edge connected graph. Based on our @math -approximation algorithm, we present an @math -approximation algorithm for the metric-cost node-connectivity survivable network design problem, where @math denotes the maximum requirement over all pairs of nodes. Our results contrast with the case of edge costs of 0 or 1, where Kortsarz, Krauthgamer, and Lee. [SIAM J. Comput., 33 (2004), pp. 704-720] recently proved, assuming NP @math DTIME( @math ), a hardness-of-approximation lower bound of @math for the subset @math -node-connectivity problem, where @math denotes a small positive number. Let @math denote the failure function of the Knuth-Morris-Pratt algorithm for a word w. In this paper we study the following problem: given an integer array @math , is there a word w over an arbitrary alphabet Σ such that @math for all i? Moreover, what is the minimum cardinality of Σ required? We give an elementary and self-contained @math time algorithm for this problem, thus improving the previously known solution ( in Conference in honor of Donald E. Knuth, 2007), which had no polynomial time bound. Using both deeper combinatorial insight into the structure of ?? and advanced algorithmic tools, we further improve the running time to @math . On a @math chessboard, the maximum number of nonattacking kings that can be placed is @math , since each @math cell can have at most one king. Let @math denote the number of ways that @math nonattacking kings can be placed on a @math chessboard. The purpose of this paper is to prove the following result. Theorem. For each @math there are constants @math , @math , and $0 We introduce a family of directed geometric graphs, denoted @math , that depend on two parameters @math and @math . For @math and @math , the @math graph is a strong @math -spanner, with @math . The out-degree of a node in the @math graph is at most @math . Moreover, we show that routing can be achieved locally on @math . Next, we show that all strong @math -spanners are also @math -spanners of the unit disk graph. Simulations for various values of the parameters @math and @math indicate that for random point sets, the spanning ratio of @math is better than the proven theoretical bounds. This paper is concerned with the problem min @math where X is a convex subset of a linear space H, and f is a smooth real-valued function on H. We propose the class of methods @math , where P denotes projection on X with respect to a Hilbert space norm @math , @math denotes the Frechet derivative of f at @math with respect to another Hilbert space norm @math on H, and @math is a positive scalar stepsize. We thus remove an important restriction in the original proposal of Goldstein [1] and Levitin and Poljak [2], where the norms @math and @math must be the same. It is therefore possible to match the norm @math with the structure of X so that the projection operation is simplified while at the same time reserving the option to choose @math on the basis of approximations to the Hessian of f so as to attain a typically superlinear rate of convergence. The resulting methods are particularly attrac... We investigate the connectivity of a wireless sensor network (WSN) secured by the heterogeneous key predistribution scheme under an independent on off channel model. The heterogeneous scheme induces an inhomogeneous random key graph, denoted by @math and the on off channel model induces an Erdős-Renyi graph, denoted by @math . Hence, the overall random graph modeling the WSN is obtained by the intersection of @math and @math . We present conditions on how to scale the parameters of the intersecting graph with respect to the network size @math such that the graph i) has no isolated nodes and ii) is connected, both with high probability (whp) as the number of nodes gets large. Our results are supported by a simulation study demonstrating that i) despite their asymptotic nature, our results can in fact be useful in designing finite -node WSNs so that they achieve secure connectivity whp; and ii) despite the simplicity of the on off communication model, the probability of connectivity in the resulting WSN approximates very well the case where the disk model is used. Denote by @math the class of all triangle-free graphs on n vertices and m edges. Our main result is the following sharp threshold, which answers the question for which densities a typical triangle-free graph is bipartite. Fix e > 0 and let @math . If n 2 ≤ m ≤ (1 − e) t3, then almost all graphs in @math are not bipartite, whereas if m ≥ (1 + e)t3, then almost all of them are bipartite. For m ≥ (1 + e)t3, this allows us to determine asymptotically the number of graphs in @math . We also obtain corresponding results for Cl-free graphs, for any cycle Cl of fixed odd length. Let @math be constant, and let @math denote the set of r-regular graphs with vertex set V = 1,2,...,n . Let G be chosen randomly from @math . We prove that with high probability ( ) the cover time of a random walk on G is asymptotic to @math . An uplink–downlink two-cell cellular network is studied in which the first base station (BS) with @math antennas receives independent messages from its @math serving users, while the second BS with @math antennas transmits independent messages to its @math serving users. That is, the first and second cells operate as uplink and downlink, respectively. Each user is assumed to have a single antenna. Under this uplink–downlink setting, the sum degrees of freedom (DoFs) is completely characterized as the minimum of @math , @math , @math , and @math , where @math denotes @math . The result demonstrates that, for a broad class of network configurations, operating one of the two cells as uplink and the other cell as downlink can strictly improve the sum DoF compared with the conventional uplink or downlink operation, in which both cells operate as either uplink or downlink. The DoF gain from such uplink–downlink operation is further shown to be achievable for heterogeneous cellular networks having hotspots and with delayed channel state information. This paper investigates the information-theoretic limits of energy-harvesting (EH) channels in the finite blocklength regime. The EH process is characterized by a sequence of i.i.d. random variables with finite variances. We use the save-and-transmit strategy proposed by Ozel and Ulukus (2012) together with Shannon’s non-asymptotic achievability bound to obtain lower bounds on the achievable rates for both additive white Gaussian noise channels and discrete memoryless channels under EH constraints. The first-order terms of the lower bounds of the achievable rates are equal to @math and the second-order (backoff from capacity) terms are proportional to @math , where @math denotes the blocklength and @math denotes the capacity of the EH channel, which is the same as the capacity without the EH constraints. The constant of proportionality of the backoff term is found and qualitative interpretations are provided. Denote by an @math -component a connected @math -uniform hypergraph with @math edges and @math vertices. We prove that the expected number of creations of @math -component during a random hypergraph process tends to 1 as @math and @math tend to @math with the total number of vertices @math such that @math . Under the same conditions, we also show that the expected number of vertices that ever belong to an @math -component is approximately @math . As an immediate consequence, it follows that with high probability the largest @math -component during the process is of size @math . Our results give insight about the size of giant components inside the phase transition of random hypergraphs. For an unweighted graph @math , @math is a subgraph if @math , and @math is a Steiner graph if @math , and for any pair of vertices @math , the distance between them in @math (denoted @math ) is at least the distance between them in @math (denoted @math ). In this paper we introduce the notion of distance preserver. A subgraph (resp., Steiner graph) @math of a graph @math is a subgraph (resp., Steiner) @math -preserver of @math if for every pair of vertices @math with @math , @math . We show that any graph (resp., digraph) has a subgraph @math -preserver with at most @math edges (resp., arcs), and there are graphs and digraphs for which any undirected Steiner @math -preserver contains @math edges. However, we show that if one allows a directed Steiner (diSteiner) @math -preserver, then these bounds can be improved. Specifically, we show that for any graph or digraph there exists a diSteiner @math -preserver with @math arcs, and that this result is tight up to a constant factor. We also study @math -preserving distance labeling schemes, that are labeling schemes that guarantee precise calculation of distances between pairs of vertices that are at a distance of at least @math one from another. We show that there exists a @math -preserving labeling scheme with labels of size @math , and that labels of size @math are required for any @math -preserving labeling scheme. Most classifiers operate by selecting the maximum of an estimate of the conditional distribution @math where @math stands for the features of the instance to be classified and @math denotes its label. This often results in a hubristic bias : overconfidence in the assignment of a definite label. Usually, the observations are concentrated on a small volume but the classifier provides definite predictions for the entire space. We propose constructing conformal prediction sets which contain a set of labels rather than a single label. These conformal prediction sets contain the true label with probability @math . Our construction is based on @math rather than @math which results in a classifier that is very cautious: it outputs the null set --- meaning "I don't know" --- when the object does not resemble the training examples. An important property of our approach is that adversarial attacks are likely to be predicted as the null set or would also include the true label. We demonstrate the performance on the ImageNet ILSVRC dataset and the CelebA and IMDB-Wiki facial datasets using high dimensional features obtained from state of the art convolutional neural networks.