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:
TOEFL listening comprehension test is for human English learners whose native language is not English. This paper reports how today's machine can perform with such a test.

The listening comprehension task considered here is highly related to Spoken Question Answering (SQA) BIBREF0 , BIBREF1 . 
answer:
What is the new task proposed in this work?


question:
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.
answer:
Which algorithm is used in the UDS-DFKI system?


question:
For evaluation of clustering performance, we use Calinski-Harabasz score BIBREF42 , also known as the variance ratio criterion. CH score is defined as the ratio between the within-cluster dispersion and the between-cluster dispersion. CH score has a range of $[0, +\infty ]$ and a higher CH score corresponds to a better clustering.  For visual validation, we plot and inspect the t-Distributed Stochastic Neighbor Embedding (t-SNE) BIBREF52 and Uniform Manifold Approximation and Projection (UMAP) BIBREF53 mappings of the learned representations as well.
answer:

How do they evaluate their method?