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The 2016 SIGMORPHON Shared Task was devoted to the problem of morphological reinflection. It introduced morphological datasets for 10 languages with diverse ty-pological characteristics. The shared task drew submissions from 9 teams representing 11 institutions reflecting a variety of approaches to addressing supervised learning of reinflection. For the(More)
We present LEMMING, a modular log-linear model that jointly models lemmati-zation and tagging and supports the integration of arbitrary global features. It is trainable on corpora annotated with gold standard tags and lemmata and does not rely on morphological dictionaries or an-alyzers. LEMMING sets the new state of the art in token-based statistical(More)
We present labeled morphological segmentation—an alternative view of morphological processing that unifies several tasks. We introduce a new hierarchy of morphotactic tagsets and CHIPMUNK, a discriminative morphological segmen-tation system that, contrary to previous work, explicitly models morphotactics. We show improved performance on three tasks for all(More)
How should one apply deep learning to tasks such as morphological reinflection, which stochastically edit one string to get another? A recent approach to such sequence-to-sequence tasks is to compress the input string into a vector that is then used to generate the output string, using recurrent neural networks. In contrast, we propose to keep the(More)
This paper presents a multi-dialect, multi-genre, human annotated corpus of dialectal Arabic with data obtained from both online newspaper commentary and Twitter. Most Arabic corpora are small and focus on Modern Standard Arabic (MSA). There has been recent interest, however, in the construction of dialectal Arabic corpora (Zaidan and Callison-Burch, 2011a;(More)
a bc z _ a b x _ a ba x _ ε : z / ε : ε / ε : y / ε:x / i n s e r t z insert x s u b s t i t u t e y f o r b d e l e t e b b c x _ rea d c c :ε / 1 a ba x _ a bc x _ To demonstrate the utility of contextual edit transducers, we examine spelling errors in social media data. We report on test data how much probability mass lands on the true. We also report(More)
Languages with rich inflectional morphology exhibit lexical data sparsity, since the word used to express a given concept will vary with the syntactic context. For instance , each count noun in Czech has 12 forms (where English uses only singular and plural). Even in large corpora, we are unlikely to observe all inflections of a given lemma. This reduces(More)
Arabic is not just one language, but rather a collection of dialects in addition to Modern Standard Arabic (MSA). While MSA is used in formal situations, dialects are the language of every day life. Until recently, there was very little dialectal Arabic in written form. With the advent of social-media, however, the landscape has changed. We provide the(More)
We present a model of morphological seg-mentation that jointly learns to segment and restore orthographic changes, e.g., funniest → fun-y-est. We term this form of analysis canon-ical segmentation and contrast it with the traditional surface segmentation, which segments a surface form into a sequence of substrings, e.g., funniest → funn-i-est. We derive an(More)
Canonical morphological segmentation aims to divide words into a sequence of standardized segments. In this work, we propose a character-based neural encoder-decoder model for this task. Additionally, we extend our model to include morpheme-level and lexical information through a neural reranker. We set the new state of the art for the task improving(More)