In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

The involved algorithms are detailed as follows.

CNN-C denotes the CNN with (Chinese) character embedding.

CNN-W denotes the CNN with (Chinese) word embedding.

CNN-Lex-C denotes the algorithm which also integrates polar words in CNN which is proposed by Shin et al. BIBREF24 . The (Chinese) character embedding is used.

CNN-Lex-W denotes the algorithm which also integrates polar words in CNN which is proposed by Shin et al. BIBREF24 . The (Chinese) word embedding is used.

Bi-LSTM-C denotes the BI-LSTM with (Chinese) character embedding.

Bi-LSTM-W denotes the Bi-LSTM with (Chinese) word embedding.

Lex-rule denotes the rule-based approach shows in Fig. 1. This approach is unsupervised.

BOW denotes the conventional algorithm which is based of bag-of-words features.
What are the other models they compare to?