Syllabification does not seem to improve word-level RNN language modeling quality when compared to character-based segmentation. However, our best syllable-aware language model, achieving performance comparable to the competitive character-aware model, has 18 -33 fewer parameters and is trained 1.2-2.2 times faster.
We have recently come across a concurrent paper @cite_11 where the authors systematically compare different subword units (characters, character trigrams, BPE @cite_7 , morphemes) and different representation models (CNN, Bi-LSTM, summation) on languages with various morphological typology. However, they do not consider syllables, and they experiment with relatively small models on small data sets (0.6M--1.4M tokens).
@cite_7 @cite_11
2611767671 274041255 1938755728 2951559648
Words can be represented by composing the representations of subword units such as word segments, characters, and or character n-grams. While such representations are effective and may capture the morphological regularities of words, they have not been systematically compared, and it is not understood how they interact with different morphological typologies. On a language modeling task, we present experiments that systematically vary (1) the basic unit of representation, (2) the composition of these representations, and (3) the morphological typology of the language modeled. Our results extend previous findings that character representations are effective across typologies, and we find that a previously unstudied combination of character trigram representations composed with bi-LSTMs outperforms most others. But we also find room for improvement: none of the character-level models match the predictive accuracy of a model with access to true morphological analyses, even when learned from an order of magnitude more data. We present statistical models for morphological disambiguation in agglutinative languages, with a specific application to Turkish. Turkish presents an interesting problem for statistical models as the potential tag set size is very large because of the productive derivational morphology. We propose to handle this by breaking up the morphosyntactic tags into inflectional groups, each of which contains the inflectional features for each (intermediate) derived form. Our statistical models score the probability of each morphosyntactic tag by considering statistics over the individual inflectional groups and surface roots in trigram models. Among the four models that we have developed and tested, the simplest model ignoring the local morphotactics within words performs the best. Our best trigram model performs with 93.95 accuracy on our test data getting all the morhosyntactic and semantic features correct. If we are just interested in syntactically relevant features and ignore a very small set of semantic features, then the accuracy increases to 95.07 . We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long short-term memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-of-the-art despite having 60 fewer parameters. On languages with rich morphology (Arabic, Czech, French, German, Spanish, Russian), the model outperforms word-level morpheme-level LSTM baselines, again with fewer parameters. The results suggest that on many languages, character inputs are sufficient for language modeling. Analysis of word representations obtained from the character composition part of the model reveals that the model is able to encode, from characters only, both semantic and orthographic information. We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long short-term memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-of-the-art despite having 60 fewer parameters. On languages with rich morphology (Arabic, Czech, French, German, Spanish, Russian), the model outperforms word-level morpheme-level LSTM baselines, again with fewer parameters. The results suggest that on many languages, character inputs are sufficient for language modeling. Analysis of word representations obtained from the character composition part of the model reveals that the model is able to encode, from characters only, both semantic and orthographic information.