Multi Task Deep Morphological Analyzer: Context Aware Joint Morphological Tagging and Lemma Prediction
This work addresses the challenge of syntactic trait prediction in morphologically rich languages, which is crucial for natural language processing applications in these languages, though it is incremental as it builds on existing multitask learning approaches.
The paper tackles morphological analysis for Hindi and Urdu by proposing MT-DMA, a character-level neural model that jointly predicts six morphological tags and lemmas, achieving new state-of-the-art accuracy scores across all seven tasks for both languages.
The ambiguities introduced by the recombination of morphemes constructing several possible inflections for a word makes the prediction of syntactic traits in Morphologically Rich Languages (MRLs) a notoriously complicated task. We propose the Multi Task Deep Morphological analyzer (MT-DMA), a character-level neural morphological analyzer based on multitask learning of word-level tag markers for Hindi and Urdu. MT-DMA predicts a set of six morphological tags for words of Indo-Aryan languages: Parts-of-speech (POS), Gender (G), Number (N), Person (P), Case (C), Tense-Aspect-Modality (TAM) marker as well as the Lemma (L) by jointly learning all these in one trainable framework. We show the effectiveness of training of such deep neural networks by the simultaneous optimization of multiple loss functions and sharing of initial parameters for context-aware morphological analysis. Exploiting character-level features in phonological space optimized for each tag using multi-objective genetic algorithm, our model establishes a new state-of-the-art accuracy score upon all seven of the tasks for both the languages. MT-DMA is publicly accessible: code, models and data are available at https://github.com/Saurav0074/morph_analyzer.