Filling Knowledge Gaps in a Broad-Coverage Machine Translation System

Knowledge-based machine translation (KBMT) techniques yield high quality in domains with detailed semantic models, limited vocabulary, and controlled input grammar. Scaling up along these dimensions means acquiring large knowledge resources. It also means behaving reasonably when definitive knowledge is not yet available. This paper describes how we can fill various KBMT knowledge gaps, often using robust statistical techniques. We describe quantitative and qualitative results from JAPANGLOSS, a broad-coverage Japanese-English MT system.Comment: 7 pages, Compressed and uuencoded postscript. To appear: IJCAI-9

A Scalable Model of Cerebellar Adaptive Timing and Sequencing: The Recurrent Slide and Latch (RSL) Model

From the dawn of modern neural network theory, the mammalian cerebellum has been a favored object of mathematical modeling studies. Early studies focused on the fan-out, convergence, thresholding, and learned weighting of perceptual-motor signals within the cerebellar cortex. This led in the proposals of Albus (1971; 1975) and Marr (1969) to the still viable idea that the granule cell stage in the cerebellar cortex performs a sparse expansive recoding of the time-varying input vector. This recoding reveals and emphasizes combinations (of input state variables) in a distributed representation that serves as a basis for the learned, state-dependent control actions engendered by cerebellar outputs to movement related centers. Although well-grounded as such, this perspective seriously underestimates the intelligence of the cerebellar cortex. Context and state information arises asynchronously due to the heterogeneity of sources that contribute signals to compose the cerebellar input vector. These sources include radically different sensory systems - vision, kinesthesia, touch, balance and audition - as well as many stages of the motor output channel. To make optimal use of available signals, the cerebellum must be able to sift the evolving state representation for the most reliable predictors of the need for control actions, and to use those predictors even if they appear only transiently and well in advance of the optimal time for initiating the control action. Such a cerebellar adaptive timing competence has recently been experimentally verified (Perrett, Ruiz, & Mauk, 1993). This paper proposes a modification to prior, population, models for cerebellar adaptive timing and sequencing. Since it replaces a population with a single clement, the proposed Recurrent Slide and Latch (RSL) model is in one sense maximally efficient, and therefore optimal from the perspective of scalability.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-92-J-1309, N00014-93-1-1364, N00014-95-1-0409)

How Is a Moving Target Continuously Tracked Behind Occluding Cover?

Office of Naval Research (N00014-95-1-0657, N00014-95-1-0409

Building a large ontology for machine translation

This paper describes efforts underway to construct a largescale ontology to support semantic processing in the PAN-GLOSS knowledge-base machine translation system. Because we axe aiming at broad sem~tntic coverage, we are focusing on automatic and semi-automatic methods of knowledge acquisition. Here we report on algorithms for merging complementary online resources, in particular the LDOCE and WordNet dictionaries. We discuss empirical results, and how these results have been incorporated into the PANGLOSS ontology. 1

Temporal dynamics of the neural representation of hue and luminance contrast

Hue and luminance contrast are the most basic visual features, emerging in early layers of convolutional neural networks trained to perform object categorization. In human vision, the timing of the neural computations that extract these features, and the extent to which they are determined by the same or separate neural circuits, is unknown. We addressed these questions using multivariate analyses of human brain responses measured with magnetoencephalography. We report four discoveries. First, it was possible to decode hue tolerant to changes in luminance contrast, and luminance contrast tolerant to changes in hue, consistent with the existence of separable neural mechanisms for these features. Second, the decoding time course for luminance contrast peaked 16-24 ms before hue and showed a more prominent secondary peak corresponding to decoding of stimulus cessation. These results are consistent with the idea that the brain uses luminance contrast as an updating signal to separate events within the constant stream of visual information. Third, neural representations of hue generalized to a greater extent across time, providing a neural correlate of the preeminence of hue over luminance contrast in perceptual grouping and memory. Finally, decoding of luminance contrast was more variable across participants for hues associated with daylight (orange and blue) than for anti-daylight (green and pink), suggesting that color-constancy mechanisms reflect individual differences in assumptions about natural lighting

Computational Linguistics and Machine Translation Research and Development

En aquest article es descriu breument les tendències actuals en la investigació i el desenvolupament de la traducció automàtica. Després d'una breu introducció històrica, es presenten els dos principals enfocaments que s'estan investigant: TA basada en estadística i TA basada en exemples. També es tracta l'avaluació de la TA i es conclou amb comentaris sobre la integració de la TA en el lloc de treball i en la diàctica de la llengua.Este artículo describe brevemente las tendencias actuales en la investigación y el desarrollo de la traducción automàtica. Después de una breve introducción histórica, presenta los dos principales enfoques que se estan investigando: TA basada en estadística y TA basada en ejemplos. También trata la evaluación de la TA y concluye con comentarios sobre la integración de la TA en el lugar de trabaja y en la didáctica de la lengua.In this article we briefly describe current trends in Machine Translation research and development. After a brief historical introduction, we present the two main approaches currently under investigation: Statistical MT and Example-based MT. We also discuss MT evaluation and conclude with remarks on intergrating MT into the workplace and into language pedagogy

Spartan Daily, September 28, 1951

Volume 40, Issue 3https://scholarworks.sjsu.edu/spartandaily/11590/thumbnail.jp

The Capacity of Channels with Feedback

We introduce a general framework for treating channels with memory and feedback. First, we generalize Massey's concept of directed information and use it to characterize the feedback capacity of general channels. Second, we present coding results for Markov channels. This requires determining appropriate sufficient statistics at the encoder and decoder. Third, a dynamic programming framework for computing the capacity of Markov channels is presented. Fourth, it is shown that the average cost optimality equation (ACOE) can be viewed as an implicit single-letter characterization of the capacity. Fifth, scenarios with simple sufficient statistics are described

Maturation of pyramidal cells in anterior piriform cortex may be sufficient to explain the end of early olfactory learning in rats

Studies have shown that neonate rodents exhibit high ability to learn a preference for novel odors associated with thermotactile stimuli that mimics maternal care. Artificial odors paired with vigorous strokes in rat pups younger than 10 postnatal days (P), but not older, rapidly induce an orientation-approximation behavior toward the conditioned odor in a two-choice preference test. The olfactory bulb (OB) and the anterior olfactory cortex (aPC), both modulated by norepinephrine (NE), have been identified as part of a neural circuit supporting this transitory olfactory learning. One possible explanation at the neuronal level for why the odor-stroke pairing induces consistent orientation-approximation behavior in P10, is the coincident activation of prior existent neurons in the aPC mediating this behavior. Specifically, odorstroke conditioning in P10 pups, promoting orientation-approximation behavior in the former but not in the latter. In order to test this hypothesis, we performed in vitro patch-clamp recordings of the aPC pyramidal neurons from rat pups from two age groups (P5–P8 and P14–P17) and built computational models for the OB-aPC neural circuit based on this physiological data. We conditioned the P5–P8 OB-aPC artificial circuit to an odor associated with NE activation (representing the process of maternal odor learning during mother–infant interactions inside the nest) and then evaluated the response of the OB-aPC circuit to the presentation of the conditioned odor. The results show that the number of responsive aPC neurons to the presentation of the conditioned odor in the P14–P17 OB-aPC circuit was lower than in the P5–P8 circuit, suggesting that at P14–P17, the reduced number of responsive neurons to the conditioned (maternal) odor might not be coincident with the responsive neurons for a second conditioned odor