instruction:
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.
question:
The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%.
Question: What was their performance on the dataset?
answer:
accuracy of 82.6%

question:
replacing this single GRU with two different components first component is a sentence reader second component is the input fusion layer
Question: What changes they did on input module?
answer:
For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader The second component is the input fusion layer

question:
We introduce a list of 8 different competencies that a reading system should master in order to process reviews and text documents in general. These 8 tasks require different competencies and a different level of understanding of the document to be well answered. For instance, detecting if an aspect is mentioned in a review will require less understanding of the review than predicting explicitly the rating of this aspect. Table TABREF10 presents the 8 tasks we have introduced in this dataset with an example of a question that corresponds to each task.
Question: What kind of questions are present in the dataset?
answer:
These 8 tasks require different competencies and a different level of understanding of the document to be well answered