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.

Q: Our transference model extends the original transformer model to multi-encoder based transformer architecture. The transformer architecture BIBREF12 is built solely upon such attention mechanisms completely replacing recurrence and convolutions. The transformer uses positional encoding to encode the input and output sequences, and computes both self- and cross-attention through so-called multi-head attentions, which are facilitated by parallelization. We use multi-head attention to jointly attend to information at different positions from different representation subspaces.

A: Which algorithm is used in the UDS-DFKI system?
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Q: I also performed several case studies. I obtained document embeddings, in the same latent space as the topic embeddings, by summing the posterior mean vectors INLINEFORM0 for each token, and visualized them in two dimensions using INLINEFORM1 -SNE BIBREF24 (all vectors were normalized to unit length). The state of the Union addresses (Figure FIGREF27 ) are embedded almost linearly by year, with a major jump around the New Deal (1930s), and are well separated by party at any given time period.

A: What is an example of a computational social science NLP task?
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Q: Moreover, because English does not mark grammatical gender, approaches developed for English are not transferable to morphologically rich languages that exhibit gender agreement BIBREF8 .

A:
Why does not the approach from English work on other languages?
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