Since it is practically too expensive to collect interaction data from real users for network training, we employ the method in [15] to generate synthetic user interactions for the objects in the training/validation data.
Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result
Sample text:```To train our CNN for classification, we set initial learning rate to 0.01 and optimize cross-entropy loss with Adagrad [26].```
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Sample text:```[14] collected misinformation automatically in order to prevent false information from spreading through social media.```
Sample intent: method



Sample text:```The response to a generated question will be supplied in the following conversations, which may be novel but not necessarily occur in the input as that in traditional QG (Du et al., 2017; Yuan et al., 2017; Tang et al., 2017; Wang et al., 2017; Mostafazadeh et al., 2016).```
Sample intent: method



Sample text:```have been proposed as potential alternatives to the Turing Test, specifically targeting its potential weaknesses and inadequacy [1].```
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Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result
Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method



Sample text:```To train our CNN for classification, we set initial learning rate to 0.01 and optimize cross-entropy loss with Adagrad [26].```
Sample intent: background



Sample text:```have been proposed as potential alternatives to the Turing Test, specifically targeting its potential weaknesses and inadequacy [1].```
Sample intent: background



Sample text:```Following the experimental design proposed in Yannakakis and Hallam (2007c) and Yannakakis et al. (2008) for effectively capturing the level of entertainment, the test-bed game under investigation is played in variants.```
Sample intent: background



Sample text:```[14] collected misinformation automatically in order to prevent false information from spreading through social media.```
Sample intent: method



Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result



Sample text:```…methods rely on some additional and often heuristic step to generate an initial solution, such as adversarially-trained neural networks [Conneau et al., 2018, Zhang et al., 2017a,b], robust self-learning [Artetxe et al., 2018b] or
5This can be easily verified by noting that the…```
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Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
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Sample text:```While this method is known to be generally reliable, there are some questions about the representativeness of the data used [25].```
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Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result