Sanskrit Sandhi Splitting using \boldsymbol{seq2(seq)^{2}}
Table 1: Location and split prediction accuracy of all the tools and models under comparison
['[BOLD] Model', '[BOLD] Accuracy (%)  [BOLD] Location Prediction', '[BOLD] Accuracy (%)  [BOLD] Split Prediction']
Initially, we compared only the top prediction in each list with the true output. Therefore, we relaxed this constraint and considered an output to be correct if the true split is present in the top ten predictions of the list. It is to be noted that B-RNN-A is the same as DD-RNN without the location decoder. This indicates that the attention mechanism of DD-RNN has learned to better identify the split location(s) due to its pre-training with the location decoder. Reddy et al. Their model is similar to B-RNN-A and is outperformed by our proposed DD-RNN by 6̃.47%. We also compared our proposed DD-RNN with a uni-directional LSTM with a depth of 4 Chen et al. (LSTM-4) and a Gated Recursive Neural Network with a depth of 5 Chen et al. (GRNN-5). These models were used to get state of the art results for Chinese word segmentation and their source code is made available online. Since these models can only predict the location(s) of the split(s) and cannot generate the split words themselves, we used the location prediction accuracy as the metric. DD-RNN’s precision is 35.3% and 40.3% better than LSTM-4 and GRNN-5 respectively. Conversely, we trained the DD-RNN for the Chinese word segmentation task to test the generalizability of the model. Since there are no morphological changes during segmentation in Chinese, the character decoder is redundant and the model collapses to simple seq2seq. We used the PKU dataset which is also used in Chen et al.