instruction:
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
question:
The embedding-wise convolution is to apply a convolution filter w $\in \mathbb {R}^{\mathcal {\\}k*d}$ to a window of $k$ word embeddings to generate a new feature, i.e., summarizing a local context of $k$ words. By applying the convolutional filter to all possible windows in the sentence, a feature map $c$ will be generated.
answer:
What supports the claim that injected CNN into recurent units will enhance ability of the model to catch local context and reduce ambiguities?


question:
To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements.
answer:
How big is dataset used for fine-tuning BERT?


question:
We first evaluate our model on MultiWOZ 2.0 dataset as shown in Table TABREF16. We compare with five published baselines. TRADE BIBREF3 is the current published state-of-the-art model. Table TABREF24 shows the results on WOZ $2.0$ dataset. We compare with four published baselines. SUMBT BIBREF17 is the current state-of-the-art model on WOZ 2.0 dataset.
answer:

What is current state-of-the-art model?