李东进同学的论文被ChineseCSCW 2019会议录用
实验室硕士研究生李东进同学的论文“Grading Chinese Answers on Specialty Subjective Questions”(作者:李东进、刘天元、潘韦、刘潇月、孙宇清、袁峰)被ChineseCSCW2019会议录用。
第14届全国计算机支持的协同工作与社会计算学术会议(Chinese Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2019) 将于2019年8月16 日 - 18 日在云南昆明市举行。会议由中国计算机学会(CCF)主办,协同计算专业委员会(CCF TCCC)和昆明理工大学共同承办。作为协同与社会计算领域最重要的全国性学术组织,CCF TCCC已逐步发展并凝练出CSCW、社会计算、群智协同、类人智能协同、流程与服务、协同设计和协同应用为代表的7个代表性研究方向。
ABSTRACT:It is an important task to grade answers on specialty subjective questions, which is helpful for the supervision of human review and improving the efficiency and quality of review process. Since this grading process should be performed at the same time with human review, there are only a few samples available for each question that can be provided by specialty experts before review process. We investigate the problem of grading Chinese answers on specialty subjective questions with a reference answer in this paper by proposing a grading model that combines two Bi-LSTM networks with attention mechanism. The first part is a sequence to sequence Bi-LSTM network that adopts the pretrained word embeddings as input. Since there is no embedding for some specialty words, we instead use the fine-grained word embeddings. After the max-pooling on each sentence, we adopt the mutual attention mechanism to learn the matching degree on specialty knowledge between each pair of sentences of answer and reference. Then we adopt another Bi-LSTM with max-pooling to have an overall vector. By concatenating these two vectors from answer and reference, a multilayer perceptron is adopted to predicate the scores. We adopt the real datasets on a national specialty examination to thoroughly verify the model performance against different amount of training data, network structures, pooling strategies and attention mechanisms. The experimental results show the effectiveness of our method.