Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce. Since neural sequenceto-sequence (seq2seq) models define the state of the art for morphological segmentation in high-resource settings and for (mostly) European languages, we first show that they also obtain competitive performance for Mexican polysynthetic languages in minimal-resource settings. We then propose two novel multi-task training approaches-one with, one without need for external unlabeled resources-, and two corresponding data augmentation methods, improving over the neural baseline for all languages. Finally, we explore cross-lingual transfer as a third way to fortify our neural model and show that we can train one single multi-lingual model for related languages while maintaining comparable or even improved performance, thus reducing the amount of parameters by close to 75%. We provide our morphological segmentation datasets for Mexicanero, Nahuatl, Wixarika and Yorem Nokki for future research. * *The first two authors contributed equally.
This paper presents our system for the Open Track of the CoNLL 2008 Shared Task (Surdeanu et al., 2008) in Joint Dependency Parsing 1 and Semantic Role Labelling. We use Markov Logic to define a joint SRL model and achieve a semantic F-score of 74.59%, the second best in the Open Track.
In this paper, we present a concept of service robot and a framework for its functional specification and implementation. The present discussion is grounded in Newell's system levels hierarchy which suggests organizing robotics research in three different layers, corresponding to Marr's computational, algorithmic and implementation levels, as follows: (1) the service robot proper, which is the subject of the present paper, (2) perception and action algorithms, and (3) the systems programming level. The concept of a service robot is articulated in practice through the introduction of a conceptual model for particular service robots; this consists of the specification of a set of basic robotic behaviours and a number of mechanisms for assembling such behaviours during the execution of complex tasks. The model involves an explicit representation of the task structure, allowing for deliberative reasoning and task management. The model also permits distinguishing between a robot's competence and performance, along the lines of Chomsky's corresponding distinction. We illustrate how this model can be realized in practice with two composition modes that we call static and dynamic; these are illustrated with the Restaurant Test and the General Purpose Service Robot Test of the RoboCup@Home competition, respectively. The present framework and methodology has been implemented in the robot Golem-II+, which is also described. The paper is concluded with an overall reflection upon the present concept of a service robot and its associated functional specifications, and the potential impact of such a conceptual model in the study, development and application of service robots in general.
The facial expression of angry emotion can be useful to direct the interaction between agents, especially in unclear and cluttered environments. During the presence of an angry face, a process of analysis and diagnosis is activated in the subject that notices it, which could impact its behavior toward the one who expresses the emotion. In order to study such an effect in human-robot interaction, an expressive robotics face was designed and constructed. The influence of this face on human action and attention was analyzed in two collaborative tasks. Results of a digital survey, experimental interaction, and a questionnaire indicated that anger is the best recognized universal facial expression, has a regulatory effect in human action, and induces human attention when an unclear condition arises during the task. An additional finding was that the prolonged presence of an angry face reduces its impact compared to positive expressions.
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