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]: For training data, 3 million webpages from this corpus were processed with a CCG parser to produce logical forms BIBREF10 . We also used the test set created by Krishnamurthy and Mitchell, which contains 220 queries generated in the same fashion as the training data from a separate section of ClueWeb. However, as they did not release a development set with their data, we used this set as a development set. This final test set contains 307 queries.
Question: How big is their dataset?
[A]: 3 million webpages processed with a CCG parser for training, 220 queries for development, and 307 queries for testing

[Q]: Probing into the assorted empirical results may help us discover some interesting phenomenons:

The advantage of pre-training gradually diminishes with the increase of labeled data BIBREF14, BIBREF17, BIBREF18.

Fixed representations yield better results than fine-tuning in some cases BIBREF24.

Overall, pre-training the Seq2Seq encoder outperforms pre-training the decoder BIBREF24, BIBREF17, BIBREF15, BIBREF16.
Question: What experimental phenomena are presented?
[A]: The advantage of pre-training gradually diminishes with the increase of labeled data Fixed representations yield better results than fine-tuning in some cases pre-training the Seq2Seq encoder outperforms pre-training the decoder

[Q]: For multi-modal sentiment analysis, we can simply split the image into two parts. One for the text input, and the other for the tabular data.
Question: How is Super Character method modified to handle tabular data also?
[A]:
simply split the image into two parts. One for the text input, and the other for the tabular data