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: In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST).
Question: What discourse features are used?

SOLUTION: Entity grid with grammatical relations and RST discourse relations.

PROBLEM: For the emotion recognition from text, we manually transcribe all utterances of our AMMER study. To exploit existing and available data sets which are larger than the AMMER data set, we develop a transfer learning approach. We use a neural network with an embedding layer (frozen weights, pre-trained on Common Crawl and Wikipedia BIBREF36), a bidirectional LSTM BIBREF37, and two dense layers followed by a soft max output layer. This setup is inspired by BIBREF38. We use a dropout rate of 0.3 in all layers and optimize with Adam BIBREF39 with a learning rate of $10^{-5}$ (These parameters are the same for all further experiments). We build on top of the Keras library with the TensorFlow backend. We consider this setup our baseline model.
Question: What is the baseline method for the task?

SOLUTION: For the emotion recognition from text they use described neural network as baseline.
For audio and face there is no baseline.

PROBLEM: We consider the following metrics for automatic evaluation of different submissions. Joint goal accuracy has been used as the primary metric to rank the submissions.

Active Intent Accuracy: The fraction of user turns for which the active intent has been correctly predicted.

Requested Slot F1: The macro-averaged F1 score for requested slots over all eligible turns. Turns with no requested slots in ground truth and predictions are skipped.

Average Goal Accuracy: For each turn, we predict a single value for each slot present in the dialogue state. This is the average accuracy of predicting the value of a slot correctly.

Joint Goal Accuracy: This is the average accuracy of predicting all slot assignments for a given service in a turn correctly.
Question: How are the models evaluated?

SOLUTION:
Active Intent Accuracy Requested Slot F1 Average Goal Accuracy Joint Goal Accuracy