TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
PROBLEM: We show that by determining and integrating heterogeneous set of features from different modalities aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users.
Question: What is the source of the user interaction data?

SOLUTION: Sociability from ego-network on Twitter

PROBLEM: In Table TABREF26 we report the results of our model on the three datasets standardly used for the evaluation of emotion classification, which we have described in Section SECREF3 .
Question: What was their performance on emotion detection?

SOLUTION: Answer with content missing: (Table 3) Best author's model B-M average micro f-score is 0.409, 0.459, 0.411 on Affective, Fairy Tales and ISEAR datasets respectively.

PROBLEM: Our goal is to decouple the content selection from the decoder by introducing an extra content selector. The most intuitive way is training the content selector to target some heuristically extracted contents. For example, we can train the selector to select overlapped words between the source and target BIBREF6 , sentences with higher tf-idf scores BIBREF20 or identified image objects that appear in the caption BIBREF21 . INLINEFORM0 as Latent Variable: Another way is to treat INLINEFORM1 as a latent variable and co-train selector and generator by maximizing the marginal data likelihood. By doing so, the selector has the potential to automatically explore optimal selecting strategies best fit for the corresponding generator component. Reinforce-select (RS) BIBREF24 , BIBREF9 utilizes reinforcement learning to approximate the marginal likelihood. We propose Variational Reinforce-Select (VRS) which applies variational inference BIBREF10 for variance reduction.
Question: How is the model trained to do only content selection?

SOLUTION:
target some heuristically extracted contents treat INLINEFORM1 as a latent variable and co-train selector and generator by maximizing the marginal data likelihood reinforcement learning to approximate the marginal likelihood Variational Reinforce-Select (VRS) which applies variational inference BIBREF10 for variance reduction