Handling Homographs in Neural Machine Translation

Homographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translation based on the context. However, with the advent of neural machine translation (NMT) systems, which can theoretically take into account global sentential context, one may hypothesize that this problem has been alleviated. In this paper, we first provide empirical evidence that existing NMT systems in fact still have significant problems in properly translating ambiguous words. We then proceed to describe methods, inspired by the word sense disambiguation literature, that model the context of the input word with context-aware word embeddings that help to differentiate the word sense be- fore feeding it into the encoder. Experiments on three language pairs demonstrate that such models improve the performance of NMT systems both in terms of BLEU score and in the accuracy of translating homographs.Comment: NAACL201

Learning Character-level Compositionality with Visual Features

Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words. However, in many writing systems compositionality has an effect even on the character-level: the meaning of a character is derived by the sum of its parts. In this paper, we model this effect by creating embeddings for characters based on their visual characteristics, creating an image for the character and running it through a convolutional neural network to produce a visual character embedding. Experiments on a text classification task demonstrate that such model allows for better processing of instances with rare characters in languages such as Chinese, Japanese, and Korean. Additionally, qualitative analyses demonstrate that our proposed model learns to focus on the parts of characters that carry semantic content, resulting in embeddings that are coherent in visual space.Comment: Accepted to ACL 201

The [Ne III] Jet of DG Tau and its Ionization Scenarios

Forbidden neon emission from jets of low-mass young stars can be used to probe the underlying high-energy processes in these systems. We analyze spectra of the jet of DG Tau obtained with the Very Large Telescope/X-Shooter spectrograph in 2010. [Ne III] $\lambda$3869 is clearly detected in the innermost 3" microjet and the outer knot located at $\sim$6".5. The velocity structure of the inner microjet can be decomposed into the low-velocity component (LVC) at $\sim -70$ km/s and the high-velocity component (HVC) at $\sim -180$ km/s. Based on the observed [Ne III] flux and its spatial extent, we suggest the origins of the [Ne III] emission regions and their relation with known X-ray sources along the jet. The flares from the hard X-ray source close to the star may be the main ionization source of the innermost microjet. The fainter soft X-ray source at 0".2 from the star may provide sufficient heating to help to sustain the ionization fraction against the recombination in the flow. The outer knot may be reionized by shocks faster than 100 km/s such that [Ne III] emission reappears and that the soft X-ray emission at 5".5 is produced. Velocity decomposition of the archival Hubble Space Telescope spectra obtained in 1999 shows that the HVC had been faster, with a velocity centroid of $\sim -260$ km/s. Such a decrease in velocity may potentially be explained by the expansion of the stellar magnetosphere, changing the truncation radius and thus the launching speed of the jet. The energy released by magnetic reconnections during relaxation of the transition can heat the gas up to several tens of megakelvin and provide the explanation for on-source keV X-ray flares that ionize the neon microjet

Velocity-Resolved [Ne III] from X-Ray Irradiated Sz 102 Microjets

Neon emission lines are good indicators of high-excitation regions close to a young stellar system because of their high ionization potentials and large critical densities. We have discovered [Ne III]{\lambda}3869 emission from the microjets of Sz 102, a low-mass young star in Lupus III. Spectroastrometric analyses of two-dimensional [Ne III] spectra obtained from archival high-dispersion ($R\approx 33,000$) Very Large Telescope/UVES data suggest that the emission consists of two velocity components spatially separated by ~ 0."3, or a projected distance of ~ 60 AU. The stronger redshifted component is centered at ~ +21 km/s with a line width of ~ 140 km/s, and the weaker blueshifted component at ~ -90 km/s with a line width of ~ 190 km/s. The two components trace velocity centroids of the known microjets and show large line widths that extend across the systemic velocity, suggesting their potential origins in wide-angle winds that may eventually collimate into jets. Optical line ratios indicate that the microjets are hot ($T\lesssim1.6\times10^4$ K) and ionized ($n_e\gtrsim5.7\times10^4$ cm$^{-3}$). The blueshifted component has ~ 13% higher temperature and ~ 46% higher electron density than the redshifted counterpart, forming a system of asymmetric pair of jets. The detection of the [Ne III]{\lambda}3869 line with the distinct velocity profile suggests that the emission originates in flows that may have been strongly ionized by deeply embedded hard X-ray sources, most likely generated by magnetic processes. The discovery of [Ne III]{\lambda}3869 emission along with other optical forbidden lines from Sz 102 support the picture of wide-angle winds surrounding magnetic loops in the close vicinity of the young star. Future high sensitivity X-ray imaging and high angular-resolution optical spectroscopy may help confirm the picture proposed.Comment: 33 pages, 9 figures, 2 tables; accepted for publication in the ApJ (minor typo and reference list fixed

Bayesian estimation and reconstruction of marine surface contaminant dispersion

Discharge of hazardous substances into the marine environment poses a substantial risk to both public health and the ecosystem. In such incidents, it is imperative to accurately estimate the release strength of the source and reconstruct the spatio-temporal dispersion of the substances based on the collected measurements. In this study, we propose an integrated estimation framework to tackle this challenge, which can be used in conjunction with a sensor network or a mobile sensor for environment monitoring. We employ the fundamental convection-diffusion partial differential equation (PDE) to represent the general dispersion of a physical quantity in a non-uniform flow field. The PDE model is spatially discretised into a linear state-space model using the dynamic transient finite-element method (FEM) so that the characterisation of time-varying dispersion can be cast into the problem of inferring the model states from sensor measurements. We also consider imperfect sensing phenomena, including miss-detection and signal quantisation, which are frequently encountered when using a sensor network. This complicated sensor process introduces nonlinearity into the Bayesian estimation process. A Rao-Blackwellised particle filter (RBPF) is designed to provide an effective solution by exploiting the linear structure of the state-space model, whereas the nonlinearity of the measurement model can be handled by Monte Carlo approximation with particles. The proposed framework is validated using a simulated oil spill incident in the Baltic sea with real ocean flow data. The results show the efficacy of the developed spatio-temporal dispersion model and estimation schemes in the presence of imperfect measurements. Moreover, the parameter selection process is discussed, along with some comparison studies to illustrate the advantages of the proposed algorithm over existing methods