Morphological Analysis Using a Sequence Decoder

Morphological Analysis Using a Sequence Decoder Ekin Aky¨urek∗ Erenay Dayanık∗ Deniz Yuret† Koc¸ University Artificial Intelligence Laboratory, ˙Istanbul, Turkey eakyurek13,edayanik16,dyuret@ku.edu.tr Abstract We introduce Morse, a recurrent encoder- decoder model that produces morphological analyses of each word in a sentence. The encoder turns the relevant information about the word and its context into a fixed size vector representation and the decoder gen- erates the sequence of characters for the lemma followed by a sequence of individ- ual morphological features. We show that generating morphological features individu- ally rather than as a combined tag allows the model to handle rare or unseen tags and to out- perform whole-tag models. In addition, gen- erating morphological features as a sequence rather than, for example, an unordered set allows our model to produce an arbitrary number of features that represent multiple inflectional groups in morphologically com- plex languages. We obtain state-of-the-art results in nine languages of different mor- phological complexity under low-resource, high-resource, and transfer learning settings. We also introduce TrMor2018, a new high- accuracy Turkish morphology data set. Our Morse implementation and the TrMor2018 data set are available online to support future research.1 1 Introduction Morse is a recurrent encoder-decoder model that takes sentences in plain text as input and produces both lemmas and morphological features of each word as output. Table 1 presents an example: The ambiguous Turkish word ‘‘masalı’’ has three ∗Equal contribution. †Corresponding author. 1See https://github.com/ai-ku/Morse.jl for a Morse implementation in Julia/Knet (Yuret, 2016) and https://github.com/ai-ku/TrMor2018 for the new Turkish data set. possible morphological analyses: the accusative and possessive forms of the stem ‘‘masal’’ (tale) and the +With form of the stem ‘‘masa’’ (table), all expressed with the same surface form (Oflazer, 1994). Morse attempts to output the correct analysis of each word based on its context in a sentence. Accurate morphological analysis and disam- biguation are important prerequisites for further syntactic and semantic processing, especially in morphologically complex languages. Many lan- guages mark case, number, person, and so on. using morphology, which helps discover the correct syntactic dependencies. In agglutinative languages, syntactic dependencies can even be between subword units. For example, Oflazer et al. (1999) observes that words in Turkish can have dependencies to any one of the inflectional groups of a derived word: in ‘‘mavi masalı oda’’ (room with a blue table) the adjective ‘‘mavi’’ (blue) modifies the noun root ‘‘masa’’ (table) even though the final part of speech of ‘‘masalı’’ is an adjective. This dependency would be dif- ficult to represent without a detailed analysis of morphology. We combined the following ideas to attack morphological analysis in the Morse model: • Morse does not require an external rule-based analyzer or dictionary, avoiding the parallel maintenance of multiple systems. • Morse performs lemmatization and tagging jointly by default; we also report on sepa- rating the two tasks. • Morse outputs morphological tags one fea- ture at a time, giving it the ability to learn unseen/rare tags. • Morse generates features as a variable size seqeunce rather than a fixed …

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