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.

[Q]: We adopt a DNN-based acoustic model BIBREF0 with 11 hidden layers and the alignment used to train the model is derived from a HMM-GMM model trained with SAT criterion.
Question: What are the deep learning architectures used in the task?
[A]: DNN-based acoustic model BIBREF0

[Q]: In our methodology to design new evaluation metrics for comparing reference summaries/translations to hypothesis ones, we established first-principles criteria on what a good evaluator should do. The first one is that it should be highly correlated with human judgement of similarity. The second one is that it should be able to distinguish sentences which are in logical contradiction, logically unrelated or in logical agreement. The third one is that a robust evaluator should also be able to identify unintelligible sentences. The last criteria is that a good evaluation metric should not give high scores to semantically distant sentences and low scores to semantically related sentences.
Question: What is the criteria for a good metric?
[A]: The first one is that it should be highly correlated with human judgement of similarity. The second one is that it should be able to distinguish sentences which are in logical contradiction, logically unrelated or in logical agreement. The third one is that a robust evaluator should also be able to identify unintelligible sentences. The last criteria is that a good evaluation metric should not give high scores to semantically distant sentences and low scores to semantically related sentences.

[Q]: Conversation Based Features
These features are devoted to exploit the peculiar characteristics of the dataset, which have a tree structure reflecting the conversation thread.

Text Similarity to Source Tweet: Jaccard Similarity of each tweet with its source tweet.

Text Similarity to Replied Tweet: the degree of similarity between the tweet with the previous tweet in the thread (the tweet is a reply to that tweet).

Tweet Depth: the depth value is obtained by counting the node from sources (roots) to each tweet in their hierarchy.
Question: What conversation-based features are used?
[A]:
Text Similarity to Source Tweet Text Similarity to Replied Tweet Tweet Depth