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:
The UNGA speeches dataset, compiled by Baturo et al. UNGAspeeches, contains the text from 7,507 speeches given between 1970-2015 inclusive.
Question: how many speeches are in the dataset?

Ex Output:
7,507

Ex Input:
The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging.
Question: Does the paper report the accuracy of the model?

Ex Output:
No

Ex Input:
To investigate whether our BERT-based model can transfer knowledge beyond language, we consider image features as simple visual tokens that can be presented to the model analogously to textual token embeddings. In order to make the $o_j$ vectors (of dimension $2048+4=2052$) comparable to BERT embeddings (of dimension 768), we use a simple linear cross-modal projection layer $W$ of dimensions $2052\hspace{-1.00006pt} imes \hspace{-1.00006pt}768$. The $N$ object regions detected in an image, are thus represented as $X_{img} = (W.o_1,...,W.o_N)$. Once mapped into the BERT embedding space with $W$, the image is seen by the rest of the model as a sequence of units with no explicit indication if it is a text or an image embedding.
Question: How are multimodal representations combined?

Ex Output:
The image feature vectors are mapped into BERT embedding dimensions and treated like a text sequence afterwards.