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.

Example Input: 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?
Example Output: Their accuracy in word segmentation is about 94%-97%.

Example Input: An acoustic word embedding is a function that takes as input a speech segment corresponding to a word, INLINEFORM0 , where each INLINEFORM1 is a vector of frame-level acoustic features, and outputs a fixed-dimensional vector representing the segment, INLINEFORM2 .
Question: How do they represent input features of their model to train embeddings?
Example Output: a vector of frame-level acoustic features

Example Input: In this work we train bilingual UWS models using the endangered language Mboshi as target and different well-resourced languages as aligned information. Results show that similar languages rank better in terms of segmentation performance, and that by combining the information learned by different models, segmentation is further improved. This might be due to the different language-dependent structures that are captured by using more than one language.
Question: How does the well-resourced language impact the quality of the output?
Example Output:
Results show that similar languages rank better in terms of segmentation performance, and that by combining the information learned by different models, segmentation is further improved.