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]: The ratio of correct `translations' (matches) was used as an evaluation measure. 
 Question: What evaluation metric do they use?
[A]: Accuracy


[Q]: Encoders with induced latent structures have been shown to benefit several tasks including document classification, natural language inference BIBREF12, BIBREF13, and machine translation BIBREF11.  
 Question: Is there any evidence that encoders with latent structures work well on other tasks?
[A]: Yes


[Q]: Next, we analyze why they do not perform well in this task and with this data:

[noitemsep,leftmargin=*]

Noisy data. A major challenge of this task is the discrepancy between annotations due to subjective judgement. Although this affects also detection using only text, its repercussion is bigger in more complex tasks, such as detection using images or multimodal detection.

Complexity and diversity of multimodal relations. Hate speech multimodal publications employ a lot of background knowledge which makes the relations between visual and textual elements they use very complex and diverse, and therefore difficult to learn by a neural network.

Small set of multimodal examples. Fig. FIGREF5 shows some of the challenging multimodal hate examples that we aimed to detect. But although we have collected a big dataset of $150K$ tweets, the subset of multimodal hate there is still too small to learn the complex multimodal relations needed to identify multimodal hate. 
 Question: What is author's opinion on why current multimodal models cannot outperform models analyzing only text?
[A]:
Noisy data Complexity and diversity of multimodal relations Small set of multimodal examples