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.

Ex Input:
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?

Ex Output:
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


Ex Input:
Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De). 
 Question: What dataset do they use for experiments?

Ex Output:
English$\rightarrow $Italian/German portions of the MuST-C corpus As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De)


Ex Input:
Our model consists of two neural network modules, i.e. an extractor and abstractor. The extractor encodes a source document and chooses sentences from the document, and then the abstractor paraphrases the summary candidates.  The extractor is based on the encoder-decoder framework. We adapt BERT for the encoder to exploit contextualized representations from pre-trained transformers. We use LSTM Pointer Network BIBREF22 as the decoder to select the extracted sentences based on the above sentence representations.  Our abstractor is practically identical to the one proposed in BIBREF8. 
 Question: What's the method used here?

Ex Output:

Two neural networks: an extractor based on an encoder (BERT) and a decoder (LSTM Pointer Network BIBREF22) and an abstractor identical to the one proposed in BIBREF8.