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]: Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations. The translation is then totally mislead.  
 Question: What misbehavior is identified?
[A]: if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations


[Q]: We use a standard set of supervised as well as unsupervised benchmark tasks from the literature to evaluate our trained models, following BIBREF16 Sentence embeddings are evaluated for various supervised classification tasks as follows. The predefined training split is used to tune the L2 penalty parameter using cross-validation and the accuracy and F1 scores are computed on the test set.  We perform unsupervised evaluation of the learnt sentence embeddings using the sentence cosine similarity, on the STS 2014 BIBREF31 and SICK 2014 BIBREF32 datasets. These similarity scores are compared to the gold-standard human judgements using Pearson's INLINEFORM0 BIBREF33 and Spearman's INLINEFORM1 BIBREF34 correlation scores.  
 Question: What metric is used to measure performance?
[A]: Accuracy and F1 score for supervised tasks, Pearson's and Spearman's correlation for unsupervised tasks


[Q]: The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061. 
 Question: Which dimensionality do they use for their embeddings?
[A]:
1061