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: First we are comparing BiLSTMBIBREF14, BiLSTM+CNNBIBREF20, BiLSTM+CRFBIBREF1, BiLSTM+CNN+CRFBIBREF2 models with CNN modelBIBREF0 and Stanford CRF modelBIBREF21.
Question: What is the baseline?

SOLUTION: CNN modelBIBREF0 Stanford CRF modelBIBREF21

PROBLEM: For a word belonging to any one of the word-groups representing these concepts, the modified cost term favors an increase for the value of this word's embedding vector dimension corresponding to the concept that the particular word belongs to.
Question: Along which dimension do the semantically related words take larger values?

SOLUTION: dimension corresponding to the concept that the particular word belongs to

PROBLEM: The two popular segmentation methods are morpheme segmentation BIBREF4 and Byte Pair Encoding (BPE) BIBREF5. After word segmentation, we additionally add an specific symbol behind each separated subword unit, which aims to assist the NMT model to identify the morpheme boundaries and capture the semantic information effectively. We utilize the Zemberek with a morphological disambiguation tool to segment the Turkish words into morpheme units, and utilize the morphology analysis tool BIBREF12 to segment the Uyghur words into morpheme units.
Question: How does the word segmentation method work?

SOLUTION:
morpheme segmentation BIBREF4 and Byte Pair Encoding (BPE) BIBREF5 Zemberek BIBREF12