instruction:
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.
question:
KGR10, also known as plWordNet Corpus 10.0 (PLWNC 10.0), is the result of the work on the toolchain to automatic acquisition and extraction of the website content, called CorpoGrabber BIBREF19 . It is a pipeline of tools to get the most relevant content of the website, including all subsites (up to the user-defined depth). The proposed toolchain can be used to build a big Web corpus of text documents. It requires the list of the root websites as the input. Tools composing CorpoGrabber are adapted to Polish, but most subtasks are language independent. The whole process can be run in parallel on a single machine and includes the following tasks: download of the HTML subpages of each input page URL with HTTrack, extraction of plain text from each subpage by removing boilerplate content (such as navigation links, headers, footers, advertisements from HTML pages) BIBREF20 , deduplication of plain text BIBREF20 , bad quality documents removal utilising Morphological Analysis Converter and Aggregator (MACA) BIBREF21 , documents tagging using Wrocaw CRF Tagger (WCRFT) BIBREF22 . Last two steps are available only for Polish.
Question: How was the KGR10 corpus created?
answer:
most relevant content of the website, including all subsites

question:
We propose a new unsupervised model, Sent2Vec, for learning universal sentence embeddings Formally, we learn a source (or context) embedding INLINEFORM0 and target embedding INLINEFORM1 for each word INLINEFORM2 in the vocabulary, with embedding dimension INLINEFORM3 and INLINEFORM4 as in ( EQREF6 ). The sentence embedding is defined as the average of the source word embeddings of its constituent words, as in ( EQREF8 ). We augment this model furthermore by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words, i.e., the sentence embedding INLINEFORM5 for INLINEFORM6 is modeled as DISPLAYFORM0
Question: How do the n-gram features incorporate compositionality?
answer:
by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words

question:
In this case, the decoding function is a linear projection, which is $= f_{ ext{de}}()=+ $ , where $\in ^{d_ imes d_}$ is a trainable weight matrix and $\in ^{d_ imes 1}$ is the bias term. A family of bijective transformation was designed in NICE BIBREF17 , and the simplest continuous bijective function $f:^D
ightarrow ^D$ and its inverse $f^{-1}$ is defined as:

$$h: \hspace{14.22636pt} _1 &= _1, & _2 &= _2+m(_1)
onumber \ h^{-1}: \hspace{14.22636pt} _1 &= _1, & _2 &= _2-m(_1)
onumber $$ (Eq. 15)

where $_1$ is a $d$ -dimensional partition of the input $\in ^D$ , and $m:^d
ightarrow ^{D-d}$ is an arbitrary continuous function, which could be a trainable multi-layer feedforward neural network with non-linear activation functions. It is named as an 'additive coupling layer' BIBREF17 , which has unit Jacobian determinant. To allow the learning system to explore more powerful transformation, we follow the design of the 'affine coupling layer' BIBREF24 :

$$h: \hspace{5.69046pt} _1 &= _1, & _2 &= _2 \odot ext{exp}(s(_1)) + t(_1)
onumber \ h^{-1}: \hspace{5.69046pt} _1 &= _1, & _2 &= (_2-t(_1)) \odot ext{exp}(-s(_1))
onumber $$ (Eq. 16)

where $s:^d
ightarrow ^{D-d}$ and $t:^d
ightarrow ^{D-d}$ are both neural networks with linear output units.

The requirement of the continuous bijective transformation is that, the dimensionality of the input $$ and the output $$ need to match exactly.
Question: What are the two decoding functions?
answer:
a linear projection and a bijective function with continuous transformation though 'affine coupling layer' of (Dinh et al.,2016).