TASK DEFINITION: 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.
PROBLEM:  We use standard Precision, Recall and F1 at mention level (Micro) and at the document level (Macro) as measurements. We can see the average linking precision (Micro) of WW is lower than that of TAC2010, and NCEL outperforms all baseline methods in both easy and hard cases. 
 Question: How do they verify generalization ability?

SOLUTION: By calculating Macro F1 metric at the document level.

PROBLEM: 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] \rightarrow \lbrace 0, 1, .., N\rbrace $ is simply defined as $q_N(x) = \lfloor {x \times N}\rfloor $. 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

SOLUTION: They introduce new trigonometric encoding which besides information about position uses additional length information (abs or relative).

PROBLEM: Motivated by phrase based SMT, we retrieve neighbors which have high local, sub-sentence level overlap with the source sentence. We adapt our approach to retrieve n-grams instead of sentences. We represent every sentence by their reduced n-gram set. For every n-gram in INLINEFORM0 , we find the closest n-gram in the training set using the IDF similarity defined above. 
 Question: Where do they retrieve neighbor n-grams from in their approach?

SOLUTION:
represent every sentence by their reduced n-gram set