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: We chose SVMhmm BIBREF111 implementation of Structural Support Vector Machines for sequence labeling. SVM implementation of   
 Question: Which machine learning methods are used in experiments?

SOLUTION: Structural Support Vector Machine

PROBLEM: The second series of tests consisted of using all the documents of the subcorpus “specialist” $E$, because the documents of the subcorpus of Annodis are not identical.  As far as system evaluations are concerned, we use the 78 $E$ documents as reference.  We calculate the precision $P$, the recall $R$ and the $F$-score on the text corpus used in our tests, as follow: 
 Question: How is segmentation quality evaluated?

SOLUTION:
Segmentation quality is evaluated by calculating the precision, recall, and F-score of the automatic segmentations in comparison to the segmentations made by expert annotators from the ANNODIS subcorpus.