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.

Input: Consider Input: To address this drawback in ROUGE, we propose a new evaluation metric: Critical Information Completeness (CIC). Formally, CIC is a recall of semantic slot information between a candidate summary and a reference summary. CIC is defined as follows:

where $V$ stands for a set of delexicalized values in the reference summary, $Count_{match}(v)$ is the number of values co-occurring in the candidate summary and reference summary, and $m$ is the number of values in set $V$. In our experiments, CIC is computed as the arithmetic mean over all the dialog domains to retain the overall performance.

CIC is a suitable complementary metric to ROUGE because it accounts for the most important information within each dialog domain. CIC can be applied to any summarization task with predefined essential entities. 
 Question: How does new evaluation metric considers critical informative entities?

Output: Answer with content missing: (formula for CIC) it accounts for the most important information within each dialog domain. CIC can be applied to any summarization task with predefined essential entities


Input: Consider Input: To discover topics from the collected tweets, we used a topic modeling approach that fuzzy clusters the semantically related words such as assigning “diabetes", “cancer", and “influenza" into a topic that has an overall “disease" theme BIBREF44 , BIBREF45 . Among topic models, Latent Dirichlet Allocation (LDA) BIBREF49 is the most popular effective model BIBREF50 , BIBREF19 as studies have shown that LDA is an effective computational linguistics model for discovering topics in a corpus BIBREF51 , BIBREF52 . We used the Mallet implementation of LDA BIBREF49 , BIBREF56 with its default settings to explore opinions in the tweets. 
 Question: How were topics of interest about DDEO identified?

Output: using topic modeling model Latent Dirichlet Allocation (LDA)


Input: Consider Input: jiant provides support for cutting-edge sentence encoder models, including support for Huggingface's Transformers. Supported models include: BERT BIBREF17, RoBERTa BIBREF27, XLNet BIBREF28, XLM BIBREF29, GPT BIBREF30, GPT-2 BIBREF31, ALBERT BIBREF32 and ELMo BIBREF33. jiant also supports the from-scratch training of (bidirectional) LSTMs BIBREF34 and deep bag of words models BIBREF35, as well as syntax-aware models such as PRPN BIBREF36 and ON-LSTM BIBREF37. jiant also supports word embeddings such as GloVe BIBREF38. 
 Question: Is jiant compatible with models in any programming language?


Output: Yes