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: 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 best baseline model is NO-MOVE, reaching an accuracy of 30.3% on single sentences and 0.3 on complete paragraphs.
Question: How well did the baseline perform?

SOLUTION: accuracy of 30.3% on single sentences and 0.3 on complete paragraphs

PROBLEM: In this section, we evaluate the proposed method intrinsically in terms of whether the co-occurrence matrix after the low-rank approximation is able to capture similar concepts on student response data sets, and also extrinsically in terms of the end task of summarization on all corpora. In the following experiments, summary length is set to be the average number of words in human summaries or less. An alternative way to evaluate the hypothesis is to let humans judge whether two bigrams are similar or not, which we leave for future work.
Question: Do they quantitavely or qualitatively evalute the output of their low-rank approximation to verify the grouping of lexical items?

SOLUTION:
They evaluate quantitatively.