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]:  Impact votes are provided by the users of the platform to evaluate how impactful a particular claim is. Users can pick one of 5 possible impact labels for a particular claim: no impact, low impact, medium impact, high impact and very high impact. While evaluating the impact of a claim, users have access to the full argument context and therefore, they can assess how impactful a claim is in the given context of an argument. 
 Question: What annotations are available in the dataset?
[A]: 5 possible impact labels for a particular claim: no impact, low impact, medium impact, high impact and very high impact


[Q]: We decided to use sentences involving at least one race- or gender-associated word. The sentences were intended to be short and grammatically simple. We also wanted some sentences to include expressions of sentiment and emotion, since the goal is to test sentiment and emotion systems. 
 Question: What criteria are used to select the 8,640 English sentences?
[A]: Sentences involving at least one race- or gender-associated word,  sentence  have to be short and grammatically simple,  sentence have to  include expressions of sentiment and emotion.


[Q]: Open-domain chatbots are more generic dialogue systems. An example is the Poly-encoder from BIBREF7 humeau2019real. It outperforms the Bi-encoder BIBREF8, BIBREF9 and matches the performance of the Cross-encoder BIBREF10, BIBREF11 while maintaining reasonable computation time. It performs strongly on downstream language understanding tasks involving pairwise comparisons, and demonstrates state-of-the-art results on the ConvAI2 challenge BIBREF12. Feed Yourself BIBREF13 is an open-domain dialogue agent with a self-feeding model. When the conversation goes well, the dialogue becomes part of the training data, and when the conversation does not, the agent asks for feedback. Lastly, Kvmemnn BIBREF14 is a key-value memory network with a knowledge base that uses a key-value retrieval mechanism to train over multiple domains simultaneously. We use all three of these models as baselines for comparison.  We compare against four dialogue system baselines: Kvmemnn, Feed Yourself, Poly-encoder, and a BERT bi-ranker baseline trained on the Persona-Chat dataset using the same training hyperparameters (including learning rate scheduler and length capping settings) described in Section SECREF20. 
 Question: What baseline models are used?
[A]:
the Poly-encoder from BIBREF7 humeau2019real Feed Yourself BIBREF13 is an open-domain dialogue agent with a self-feeding model Kvmemnn BIBREF14 is a key-value memory network with a knowledge base that uses a key-value retrieval mechanism to train over multiple domains simultaneously We compare against four dialogue system baselines: Kvmemnn, Feed Yourself, Poly-encoder, and a BERT bi-ranker baseline trained on the Persona-Chat dataset using the same training hyperparameters (including learning rate scheduler and length capping settings) described in Section SECREF20. a BERT bi-ranker