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: We experiment with two image captioning models: the Show&Tell model BIBREF0 and the LRCN1u model BIBREF1. Both models follow the basic encoder-decoder architecture design that uses a CNN encoder to condense the visual information into an image embedding, which in turn conditions an LSTM decoder to generate a natural language caption. The main difference between the two models is the way they condition the decoder. The Show&Tell model feeds the image embedding as the “predecessor word embedding” to the first produced word, while the LRCN1u model concatenates the image features with the embedded previous word as the input to the sequence model at each time step. 
 Question: Which existing models are evaluated?

SOLUTION: Show&Tell and LRCN1u

PROBLEM: All cases exhibit high scores—in the vast majority of the cases substantially higher than reported in previous work. In particular, in BIBREF1 we assess the ability of LSTMs to learn subject-verb agreement patterns in English, and evaluate on naturally occurring wikipedia sentences.  BIBREF2 also consider subject-verb agreement, but in a “colorless green ideas” setting in which content words in naturally occurring sentences are replaced with random words with the same part-of-speech and inflection, thus ensuring a focus on syntax rather than on selectional-preferences based cues.  BIBREF3 consider a wider range of syntactic phenomena (subject-verb agreement, reflexive anaphora, negative polarity items) using manually constructed stimuli, allowing for greater coverage and control than in the naturally occurring setting. 
 Question: Were any of these tasks evaluated in any previous work?

SOLUTION: Yes

PROBLEM: This work analyzes the understanding of pretrained language models of factual and commonsense knowledge stored in negated statements. To this end, we introduce the negated LAMA dataset. We construct it by simply inserting negation elements (e.g., “not”) in LAMA cloze statement (e.g., “The theory of relativity was not developed by [MASK].”). 
 Question: How did they extend LAMA evaluation framework to focus on negation?

SOLUTION:
To this end, we introduce the negated LAMA dataset. We construct it by simply inserting negation elements (e.g., “not”) in LAMA cloze statement