Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit
Table 1.A: WPT on DCS4k
['System', 'P', 'R', 'F', 'PM']
The proposed model, Clique-EBM (System 8), outperforms all the other models across all the 4 metrics on both the tasks. Clique-EBM shows an improvement of 7.06% and 40.79% in terms of F-score and perfect matching from the current state of the art (System 6) in WPT. Currently there exists no system that predicts the morphological tags for a given word in Sanskrit. For WP3T, Clique-EBM has shown a percentage increase of 11.64% and 69.65% in F-Score and the perfect matching score from Tree-EBM, the next best system. Out of which 3, 4, 5, 7 and 8 use the edge vectors, which encodes the morphological constraints refined using PRA Lao and Cohen As a result these systems require \textless10% training data than required by system 6. System 3 was trained on 10,000 sentences, while systems 4 and 5 were trained on 9,000 sentences after which the models got saturated. Systems 7 and 8 were trained on 8,200 sentences which is 7.66% of the training data (107,000) used in System 6. In terms of training time, \newcitereddy2018seg reports a training time of 12 hours on a GPU machine, while systems 7 and 8 take a training time of 8 hours on an Intel Xeon CPU based machine with 24 cores and 256 GB RAM. For systems 4 and 5 it takes roughly 4 hours to train on the same machine. There was no training involved for the prediction of segmentations in system 2. It can further be observed that such common root identical surface forms often have one or more common grammatical categories in their morphological classes Goyal and Huet The grammatical categories in a morphological class are indicative of the syntactic roles and the morphological agreement between the words in a construction. We empirically observe that the inference procedure for clique-EBM, which considers the entire input context and pairwise potentials between the candidates, helps in improving the performance of the model. A similar observation regarding incompatible morphological agreements between predicted words was made by \newcitehassan2018achieving for their NMT model. The authors introduced an elaborate 2 phase decoder and a KL-Divergence based regularisation to combat the issue.