LEARNING HIERARCHICAL TRANSLATION STRUCTURE

annotations but shaped and trained for translation.We start by labelling each phrase-pair span in theword-aligned training data with multiple linguisti-cally motivated categories, offering multi-grainedabstractions from its lexical content. These phrase-pair label charts are the input of our[r]

ing and Garrod, 2004; Halliday and Hasan, 1976) onthe one hand, and variation (to improve text qualityand readability) on the other hand (Belz and Reiter,2006; Foster and Oberlander, 2006) in a 50/50 dis-tribution. We evaluate the learnt surface realisationdecisions in terms of similarity with human[r]

New material is presented in the form of a dialogue. Based on the principle that language learning is habit formation, the method fosters dependence on mimicry, memorization of set phrases and over-learning. Structures are sequenced and taught one at a time. Structural patterns are tau[r]

X2YSpurious edgeX1X2Y17Graphical modelsApproaches to learning of graphical model structureConstraint-based approaches Identify a set of conditional independence properties Identify the network structure that best satisfies these constraints Limitation: sensitive to errors[r]

test sets show that the HD-HPB model signifi-cantly outperforms Chiang’s model with aver-age gains of 1.91 points absolute in BLEU.1 IntroductionChiang’s hierarchical phrase-based (HPB) transla-tion model utilizes synchronous context free gram-mar (SCFG) for translation derivation (Chia[r]

Hints for successful individualized learning: Provide structure for individualized learning through templates, help sessions, or individual conferences Plan sufficient time for thorough [r]

our model works under the new defined SCFG withchunks.Besides, some other researchers make efforts onthe tree-to-string model by employing exponentiallyalternative parses to alleviate the drawback of 1-bestparse. Mi et al. (2008) presented forest-based trans-lation where the decoder translates a pack[r]

flexible and robust and eliminates the need for multiple clusterhead selection rounds.Forster and Murphy focused on the clusterheads selection process and assumed that clustersare predefined (rectangular grids) and that each node knows the identity of the cluster towhich it belongs. They targeted a tr[r]

flexible and robust and eliminates the need for multiple clusterhead selection rounds.Forster and Murphy focused on the clusterheads selection process and assumed that clustersare predefined (rectangular grids) and that each node knows the identity of the cluster towhich it belongs. They targeted a tr[r]

lamellae on the nanometer scale to the crystal structure on the subnanometer scale of a transparent EFA fiber.37 We suggested that the crystal structure of the crystalline fluorinated polymers such as PTFE, EFA, PFA, and so on, form the orthorhombic system. The crystalline fiber of EFA[r]

FeatureYour Org. ScoreDescription of Matching Personality TraitsIPIP-Neo Measure Your IPIP Ave. ScoreHigh ability to influence (3) Low ability to influence (1)Challenges authority, likes to be empoweredRespect for hierarchical structure, needs direction in lifeHigh liberalism (3) High[r]

are (Turney, 2002; Dave et al., 2003; Hu and Liu,2004; Liu et al., 2005; Popescu and Etzioni, 2005;Zhuang et al., 2006; Lu and Zhai, 2008; Titov andMcDonald, 2008; Zhou and Chaovalit, 2008; Lu etal., 2009), there is still room for improvement ontackling this problem. When we look into the de-tails o[r]

(Hofmann, 1999) are types of topic models. LDAis the most common topic model currently in use,therefore we exploit it for mining topics in this pa-per. Here, we first give a brief description of LDA.LDA views each document as a mixture pro-portion of various topics, and generates each wordby multinom[r]

References R.K. Ando and T. Zhang. 2005. A framework for learn- ing predictive structures from multiple tasks and un- labeled data. Journal of Machine Learning Research, 6, pp. 1817-1853. U. Aydın, S. Murat, Olcay T Yıldız, A. Ethem, 2009, Incremental construction of classifier and discrimina[r]

An Ecient Signaling Structure for ATM NetworksDakang WuDepartment of Computer ScienceWashington UniversitySt. Louis, Missouri 63130-4899AbstractAs ATM becomes widely accepted as the communi-cation standard for high speed networks, the signalingsystem structure and protocols that suppo[r]

control of the skeleton model  The skin follows the movement of the skeleton Representation of postures How can we specify the posture of the avatars? The body has a lot of degrees of freedom And has a hierarchical structure Hierarchical structure of the body The pos[r]

It is obvious that in GPA, the workload for finding thebest pairwise synchronization sequence is shared among thereference node and the other parent nodes, that is, no net-workwide heuristic connectivity search is required for GPA.Notice that in the example of Figure 5(a), the network syn-chronized u[r]

that “syntactic constituency” may be too coarse a no-tion by itself; rather, there is value in taking a finer-grained approach, and in allowing the model to de-cide how far to trust each element of the syntacticanalysis as part of the system’s optimization process.AcknowledgmentsThis work was support[r]

Traditional teachers often focus on grammatical rules rather than meaning when teaching a grammatical structure as they believe that learning a foreign language is about learning to mast[r]

|pa(c(yi)h))where y denotes a set of values of all network variables, pa(yi) and c(yi) are valuesof the parents and children of Yi, pa(c(yi)h) are values of the parents of the hth childof Yi, and the symbol \ denotes the set difference.10.4 LearningLearning a Bayesian network from data consists of t[r]