@Article{cmc.2020.011969,
AUTHOR = {Weifeng Ma, Jiao Lou, Caoting Ji, Laibin Ma},
TITLE = {ACLSTM: A Novel Method for CQA Answer Quality Prediction Based on Question-Answer Joint Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {66},
YEAR = {2021},
NUMBER = {1},
PAGES = {179--193},
URL = {http://www.techscience.com/cmc/v66n1/40440},
ISSN = {1546-2226},
ABSTRACT = {Given the limitations of the community question answering (CQA)
answer quality prediction method in measuring the semantic information of the
answer text, this paper proposes an answer quality prediction model based on
the question-answer joint learning (ACLSTM). The attention mechanism is used
to obtain the dependency relationship between the Question-and-Answer (Q&A)
pairs. Convolutional Neural Network (CNN) and Long Short-term Memory Network (LSTM) are used to extract semantic features of Q&A pairs and calculate
their matching degree. Besides, answer semantic representation is combined with
other effective extended features as the input representation of the fully connected
layer. Compared with other quality prediction models, the ACLSTM model can
effectively improve the prediction effect of answer quality. In particular, the mediumquality answer prediction, and its prediction effect is improved after adding effective extended features. Experiments prove that after the ACLSTM model learning,
the Q&A pairs can better measure the semantic match between each other, fully
reflecting the model’s superior performance in the semantic information processing
of the answer text.},
DOI = {10.32604/cmc.2020.011969}
}