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 Q]: Methods ::: Length Encoding Method
Inspired by BIBREF11, we use length encoding to provide the network with information about the remaining sentence length during decoding. We propose two types of length encoding: absolute and relative. Let pos and len be, respectively, a token position and the end of the sequence, both expressed in terms of number characters. Then, the absolute approach encodes the remaining length:

where $i=1,\ldots ,d/2$.

Similarly, the relative difference encodes the relative position to the end. This representation is made consistent with the absolute encoding by quantizing the space of the relative positions into a finite set of $N$ integers:

where $q_N: [0, 1]
ightarrow \lbrace 0, 1, .., N
brace $ is simply defined as $q_N(x) = \lfloor {x imes N}
floor $. As we are interested in the character length of the target sequence, len and pos are given in terms of characters, but we represent the sequence as a sequence of BPE-segmented subwords BIBREF17. To solve the ambiguity, len is the character length of the sequence, while pos is the character count of all the preceding tokens.
Question: How do they enrich the positional embedding with length information
[EX A]: They introduce new trigonometric encoding which besides information about position uses additional length information (abs or relative).

[EX Q]: We choose Zulu (ZUL), a Bantoid language. In contrast to the first two, it is strongly prefixing.
Question: What is an example of a prefixing language?
[EX A]: Zulu

[EX Q]: The clinical notes we used for the experiment are provided by domain experts, consisting of 1,160 physician logs of Medical ICU admission requests at a tertiary care center affiliated to Mount Sanai. The enriched corpus contains 42,506 Wikipedia articles, each of which corresponds to one candidate, 6 research papers and 2 critical care medicine textbooks, besides our raw ICU data.
Question: Which dataset do they use to build their model?
[EX A]:
1,160 physician logs of Medical ICU admission requests 42,506 Wikipedia articles 6 research papers and 2 critical care medicine textbooks