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.

Ex Input:
1. BERT based fine-tuning: In the first approach, which is shown in Figure FIGREF8, very few changes are applied to the BERTbase. In this architecture, only the [CLS] token output provided by BERT is used. The [CLS] output, which is equivalent to the [CLS] token output of the 12th transformer encoder, a vector of size 768, is given as input to a fully connected network without hidden layer. The softmax activation function is applied to the hidden layer to classify.

2. Insert nonlinear layers: Here, the first architecture is upgraded and an architecture with a more robust classifier is provided in which instead of using a fully connected network without hidden layer, a fully connected network with two hidden layers in size 768 is used. The first two layers use the Leaky Relu activation function with negative slope = 0.01, but the final layer, as the first architecture, uses softmax activation function as shown in Figure FIGREF8.

3. Insert Bi-LSTM layer: Unlike previous architectures that only use [CLS] as the input for the classifier, in this architecture all outputs of the latest transformer encoder are used in such a way that they are given as inputs to a bidirectional recurrent neural network (Bi-LSTM) as shown in Figure FIGREF8. After processing the input, the network sends the final hidden state to a fully connected network that performs classification using the softmax activation function.

4. Insert CNN layer: In this architecture shown in Figure FIGREF8, the outputs of all transformer encoders are used instead of using the output of the latest transformer encoder. So that the output vectors of each transformer encoder are concatenated, and a matrix is produced. The convolutional operation is performed with a window of size (3, hidden size of BERT which is 768 in BERTbase model) and the maximum value is generated for each transformer encoder by applying max pooling on the convolution output. By concatenating these values, a vector is generated which is given as input to a fully connected network. By applying softmax on the input, the classification operation is performed. 
 Question: What new fine-tuning methods are presented?

Ex Output:
BERT based fine-tuning Insert nonlinear layers Insert Bi-LSTM layer Insert CNN layer


Ex Input:
Posts in FBFans dataset are used for this analysis. We calculate the like statistics of each distinct author from these 32,595 posts. 
 Question: What is the size of the Chinese data?

Ex Output:
32,595 posts


Ex Input:
For task 1, we use F1-score as evaluation metric. We use manual evaluation for task 2. There are five evaluation metrics for task 2 as following.

Task completion ratio: The number of completed tasks divided by the number of total tasks.

User satisfaction degree: There are five scores -2, -1, 0, 1, 2, which denote very dissatisfied, dissatisfied, neutral, satisfied and very satisfied, respectively.

Response fluency: There are three scores -1, 0, 1, which indicate nonfluency, neutral, fluency.

Number of dialogue turns: The number of utterances in a task-completed dialogue.

Guidance ability for out of scope input: There are two scores 0, 1, which represent able to guide or unable to guide. 
 Question: What metrics are used in the evaluation?

Ex Output:

For task 1, we use F1-score Task completion ratio User satisfaction degree Response fluency Number of dialogue turns Guidance ability for out of scope input