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]: To overcome the above-mentioned challenges, we propose a locally-linear meta-embedding learning method that (a) requires only the words in the vocabulary of each source embedding, without having to predict embeddings for missing words, (b) can meta-embed source embeddings with different dimensionalities, (c) is sensitive to the diversity of the neighbourhoods of the source embeddings.

Our proposed method comprises of two steps: a neighbourhood reconstruction step (Section "Nearest Neighbour Reconstruction" ), and a projection step (Section "Projection to Meta-Embedding Space" ). In the reconstruction step, we represent the embedding of a word by the linearly weighted combination of the embeddings of its nearest neighbours in each source embedding space.
Question: What is the introduced meta-embedding method introduced in this paper?
[EX A]: proposed method comprises of two steps: a neighbourhood reconstruction step (Section "Nearest Neighbour Reconstruction" ), and a projection step (Section "Projection to Meta-Embedding Space" ). In the reconstruction step, we represent the embedding of a word by the linearly weighted combination of the embeddings of its nearest neighbours in each source embedding space.

[EX Q]: Probing into the assorted empirical results may help us discover some interesting phenomenons:

The advantage of pre-training gradually diminishes with the increase of labeled data BIBREF14, BIBREF17, BIBREF18.

Fixed representations yield better results than fine-tuning in some cases BIBREF24.

Overall, pre-training the Seq2Seq encoder outperforms pre-training the decoder BIBREF24, BIBREF17, BIBREF15, BIBREF16.
Question: What experimental phenomena are presented?
[EX A]: The advantage of pre-training gradually diminishes with the increase of labeled data Fixed representations yield better results than fine-tuning in some cases pre-training the Seq2Seq encoder outperforms pre-training the decoder

[EX Q]: We focus on the following aspects of NLP research: size, demographics, areas of research, impact, and correlation of citations with demographic attributes (age and gender).
Question: What aspect of NLP research is examined?
[EX A]:
size, demographics, areas of research, impact, and correlation of citations with demographic attributes (age and gender)