Graph-to-Sequence Learning using Gated Graph Neural Networks

Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on this setting obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and/or standard recurrent networks to achieve the best performance. In this work, we propose a new model that encodes the full structural information contained in the graph. Our architecture couples the recently proposed Gated Graph Neural Networks with an input transformation that allows nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work. Experimental results show that our model outperforms strong baselines in generation from AMR graphs and syntax-based neural machine translation.Comment: ACL 201

Exploring Prediction Uncertainty in Machine Translation Quality Estimation

Machine Translation Quality Estimation is a notoriously difficult task, which lessens its usefulness in real-world translation environments. Such scenarios can be improved if quality predictions are accompanied by a measure of uncertainty. However, models in this task are traditionally evaluated only in terms of point estimate metrics, which do not take prediction uncertainty into account. We investigate probabilistic methods for Quality Estimation that can provide well-calibrated uncertainty estimates and evaluate them in terms of their full posterior predictive distributions. We also show how this posterior information can be useful in an asymmetric risk scenario, which aims to capture typical situations in translation workflows.Comment: Proceedings of CoNLL 201

Heavy Duty Vehicles Using Clean, High Efficiency Alcohol Engines

Work sponsored by the John and Jane Bradley gift to the MIT Energy Initiative. Non-petroleum based liquid fuels are essential for reducing oil dependence and greenhouse gas generation. Increased substitution of alcohol fuel for petroleum based fuels could be achieved by 1) use in high efficiency spark ignition engines that are employed for heavy duty as well as light duty operation and 2) use of methanol as well as ethanol. Methanol is the liquid fuel that is most efficiently produced from thermo-chemical gasification of coal, natural gas, waste or biomass. Ethanol can also be produced by this process but at lower efficiency and higher cost. Coal derived methanol is in limited initial use as a transportation fuel in China. Methanol could potentially be produced from natural gas at an economically competitive fuel costs, and with essentially the same greenhouse gas impact as gasoline. Waste derived methanol could also be an affordable low carbon fuel. In this paper we describe modeling studies of alcohol fuel operation in highly turbocharged direct injection spark ignition engines operated at high compression ratio. The studies suggest that these engines could be a

Living Together: The Economics of Cohabitation

Analyzes trends among unmarried couples ages 30 to 44 living together by education. Compares median household income, poverty rate, composition of household income, and number of children with those of married couples and those without partners

Dichopetala And New Related North American Genera: A Study In Genitalic Similarity In Sympatry And Genitalic Differences In Allopatry (Tettigoniidae: Phaneropterinae: Odonturini)

The genus Dichopetala Brunner von Wattenwyl, 1878 sensu Rehn and Hebard, 1914 is revised, with the description of 6 new genera and 14 new species...http://deepblue.lib.umich.edu/bitstream/2027.42/122723/1/OLD_MP_203.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/122723/2/OLD_MP_203_sm.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/122723/3/Dichopetala Locality Database.xlshttp://deepblue.lib.umich.edu/bitstream/2027.42/122723/4/Dichopetala Tables for Diagnosis.xlshttp://deepblue.lib.umich.edu/bitstream/2027.42/122723/8/MP_203-REV2.pdfDescription of OLD_MP_203.pdf : OLD: MP 203 high res. [Superseded]Description of OLD_MP_203_sm.pdf : OLD: MP 203 lo res. [Superseded]Description of Dichopetala Locality Database.xls : DatasetDescription of Dichopetala Tables for Diagnosis.xls : Datase