TASK DEFINITION: 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.
PROBLEM: In Table TABREF15 we measure BLEU BIBREF19, NIST BIBREF20, METEOR BIBREF21, ROUGE-L BIBREF22 and CIDEr BIBREF23 metrics on the 2018 E2E NLG Challenge test data using the evaluation script provided by the organizers.  We use the gold standard selection during training and validation of the text generation model, as well as the automatic evaluation. As we deploy our text generation model for manual evaluation, we use a Conditional Random Field (CRF) model to predict which events to mention. The second human evaluation aimed at judging the acceptability of the output for production use in a news agency.  In the minimum edit evaluation, carried out by the annotator who created the news corpus, only factual mistakes and grammatical errors are corrected, resulting in text which may remain awkward or unfluent. The word error rate (WER) of the generated text compared to its corrected variant as a reference is 5.6% (6.2% disregarding punctuation).  The factual errors and their types are summarized in Table TABREF23. From the total of 510 game events generated by the system, 78 of these contained a factual error, i.e. 84.7% were generated without factual errors. Most fluency issues relate to the overall flow and structure of the report. Addressing these issues would require the model to take into account multiple events in a game, and combine the information more flexibly to avoid repetition.  The second human evaluation aimed at judging the acceptability of the output for production use in a news agency. The output is evaluated in terms of its usability for a news channel labelled as being machine-generated, i.e. not aiming at the level of a human journalist equipped with substantial background information. The evaluation was carried out by two journalists from the STT agency, who split the 59 games among themselves approximately evenly. 
 Question: What evaluation criteria and metrics were used to evaluate the generated text?

SOLUTION: BLEU  NIST  METEOR  ROUGE-L CIDEr  evaluation script automatic evaluation human evaluation minimum edit evaluation word error rate (WER) factual errors and their types fluency issues acceptability of the output for production use in a news agency

PROBLEM: A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks. 
 Question: On what model architectures are previous co-occurence networks based?

SOLUTION: in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window connects only adjacent words in the so called word adjacency networks

PROBLEM: In sub-task C the goal is to classify the target of the offensive language. Only posts labeled as targeted insults (TIN) in sub-task B are considered in this task BIBREF17 . Samples are annotated with one of the following:

Individual (IND): Posts targeting a named or unnamed person that is part of the conversation. In English this could be a post such as @USER Is a FRAUD Female @USER group paid for and organized by @USER. In Danish this could be a post such as USER du er sku da syg i hoved. These examples further demonstrate that this category captures the characteristics of cyberbullying, as it is defined in section "Background" .

Group (GRP): Posts targeting a group of people based on ethnicity, gender or sexual orientation, political affiliation, religious belief, or other characteristics. In English this could be a post such as #Antifa are mentally unstable cowards, pretending to be relevant. In Danish this could be e.g. Åh nej! Svensk lorteret!

Other (OTH): The target of the offensive language does not fit the criteria of either of the previous two categories. BIBREF17 . In English this could be a post such as And these entertainment agencies just gonna have to be an ass about it.. In Danish this could be a post such as Netto er jo et tempel over lort. 
 Question: How many categories of offensive language were there?

SOLUTION:
3