Abstract: This disclosure relates generally to quality evaluation of collaborative text input, and more particularly to system and method for quality evaluation of collaborative text inputs using Long Short Term Memory (LSTM) networks. In one embodiment, the method includes receiving an input data associated with a task to be accomplished collaboratively and sequentially by a plurality of contributors. The input data includes task-wise data sequence of contributor's post-edit submissions. A plurality of features are extracted from the input data. Based on the plurality of features, a plurality of input sequences are constructed. The input sequences include a plurality of concatenated feature vectors, where each of the concatenated feature vectors includes a post-edit feature vector and a contributor representation feature vector. The input sequences are modelled as a LSTM network, where the LSTM network is utilized to train a binary classifier for quality evaluation of the post-edit submission.

Abstract: This disclosure relates generally to quality evaluation of collaborative text input, and more particularly to system and method for quality evaluation of collaborative text inputs using Long Short Term Memory (LSTM) networks. In one embodiment, the method includes receiving an input data associated with a task to be accomplished collaboratively and sequentially by a plurality of contributors. The input data includes task-wise data sequence of contributor's post-edit submissions. A plurality of features are extracted from the input data. Based on the plurality of features, a plurality of input sequences are constructed. The input sequences include a plurality of concatenated feature vectors, where each of the concatenated feature vectors includes a post-edit feature vector and a contributor representation feature vector. The input sequences are modelled as a LSTM network, where the LSTM network is utilized to train a binary classifier for quality evaluation of the post-edit submission.