TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
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: Abstractive

PROBLEM: We introduce a surrogate training objective that avoids these problems and as a result is fully continuous. Specifically, we form a continuous function softLB that seeks to approximate the result of running our decoder on input INLINEFORM0 and then evaluating the result against INLINEFORM1 using INLINEFORM2 .
Question: Do they provide a framework for building a sub-differentiable for any final loss metric?

SOLUTION: Yes-no

PROBLEM: Spoken-SQuAD is chosen as the target domain data for training and testing. Spoken-SQuAD BIBREF5 is an automatically generated corpus in which the document is in spoken form and the question is in text form. The reference transcriptions are from SQuAD BIBREF1 . There are 37,111 and 5,351 question answer pairs in the training and testing sets respectively, and the word error rate (WER) of both sets is around 22.7%.

The original SQuAD, Text-SQuAD, is chosen as the source domain data, where only question answering pairs appearing in Spoken-SQuAD are utilized. In our task setting, during training we train the proposed QA model on both Text-SQuAD and Spoken-SQuAD training sets. While in the testing stage, we evaluate the performance on Spoken-SQuAD testing set.
Question: Which datasets did they use for evaluation?

SOLUTION:
Extractive