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 have evaluated our models considering the F1 Score, which is the harmonic mean of precision and recall. We have run ten times the experiment for each model and considered the average F1 Score. The results are mentioned in Table TABREF11. Considering F1 Macro the models that include the multi-attention mechanism outperform the others and particularly the one with the Projected Layer has the highest performance. In three out of four pairs of models, the ones with the Projected Layer achieved better performance, so in most cases the addition of the Projected Layer had a significant enhancement.

Question: Which variation provides the best results on this dataset?

SOLUTION: the model with multi-attention mechanism and a projected layer

PROBLEM: Based on annotated corpora and token-based features, studies used machine learning approaches to build word segmentation systems with accuracy about 94%-97%.
Question: How successful are the approaches used to solve word segmentation in Vietnamese?

SOLUTION: Their accuracy in word segmentation is about 94%-97%.

PROBLEM: The first model, referred as 'LSTM (PTB)' in the following sections, was trained on the sentences from Penn Treebank BIBREF12. The second model, referred as 'LSTM (FTB)', was trained on the sentences from French Treebank BIBREF13. We incorporate two pretrained English language models: one trained on the Billion Word benchmark (referred as 'LSTM (1B)') from BIBREF14, and the other trained on English Wikipedia (referred as 'LSTM (enWiki)') from BIBREF3. For French, we trained a large LSTM language model (referred as 'LSTM (frWaC)') on a random subset (about 4 million sentences, 138 million word tokens) of the frWaC dataset BIBREF15.
Question: What is the size of the datasets employed?

SOLUTION:
(about 4 million sentences, 138 million word tokens) one trained on the Billion Word benchmark