Mans Hulden


2021

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Findings of the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering
Adam Wiemerslage | Arya D. McCarthy | Alexander Erdmann | Garrett Nicolai | Manex Agirrezabal | Miikka Silfverberg | Mans Hulden | Katharina Kann
Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

We describe the second SIGMORPHON shared task on unsupervised morphology: the goal of the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering is to cluster word types from a raw text corpus into paradigms. To this end, we release corpora for 5 development and 9 test languages, as well as gold partial paradigms for evaluation. We receive 14 submissions from 4 teams that follow different strategies, and the best performing system is based on adaptor grammars. Results vary significantly across languages. However, all systems are outperformed by a supervised lemmatizer, implying that there is still room for improvement.

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SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages
Tiago Pimentel | Maria Ryskina | Sabrina J. Mielke | Shijie Wu | Eleanor Chodroff | Brian Leonard | Garrett Nicolai | Yustinus Ghanggo Ate | Salam Khalifa | Nizar Habash | Charbel El-Khaissi | Omer Goldman | Michael Gasser | William Lane | Matt Coler | Arturo Oncevay | Jaime Rafael Montoya Samame | Gema Celeste Silva Villegas | Adam Ek | Jean-Philippe Bernardy | Andrey Shcherbakov | Aziyana Bayyr-ool | Karina Sheifer | Sofya Ganieva | Matvey Plugaryov | Elena Klyachko | Ali Salehi | Andrew Krizhanovsky | Natalia Krizhanovsky | Clara Vania | Sardana Ivanova | Aelita Salchak | Christopher Straughn | Zoey Liu | Jonathan North Washington | Duygu Ataman | Witold Kieraś | Marcin Woliński | Totok Suhardijanto | Niklas Stoehr | Zahroh Nuriah | Shyam Ratan | Francis M. Tyers | Edoardo M. Ponti | Grant Aiton | Richard J. Hatcher | Emily Prud'hommeaux | Ritesh Kumar | Mans Hulden | Botond Barta | Dorina Lakatos | Gábor Szolnok | Judit Ács | Mohit Raj | David Yarowsky | Ryan Cotterell | Ben Ambridge | Ekaterina Vylomova
Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

This year's iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features. In terms of the task, we enrich UniMorph with new data for 32 languages from 13 language families, with most of them being under-resourced: Kunwinjku, Classical Syriac, Arabic (Modern Standard, Egyptian, Gulf), Hebrew, Amharic, Aymara, Magahi, Braj, Kurdish (Central, Northern, Southern), Polish, Karelian, Livvi, Ludic, Veps, Võro, Evenki, Xibe, Tuvan, Sakha, Turkish, Indonesian, Kodi, Seneca, Asháninka, Yanesha, Chukchi, Itelmen, Eibela. We evaluate six systems on the new data and conduct an extensive error analysis of the systems' predictions. Transformer-based models generally demonstrate superior performance on the majority of languages, achieving >90% accuracy on 65% of them. The languages on which systems yielded low accuracy are mainly under-resourced, with a limited amount of data. Most errors made by the systems are due to allomorphy, honorificity, and form variation. In addition, we observe that systems especially struggle to inflect multiword lemmas. The systems also produce misspelled forms or end up in repetitive loops (e.g., RNN-based models). Finally, we report a large drop in systems' performance on previously unseen lemmas.

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Applying the Transformer to Character-level Transduction
Shijie Wu | Ryan Cotterell | Mans Hulden
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

The transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks. Yet for character-level transduction tasks, e.g. morphological inflection generation and historical text normalization, there are few works that outperform recurrent models using the transformer. In an empirical study, we uncover that, in contrast to recurrent sequence-to-sequence models, the batch size plays a crucial role in the performance of the transformer on character-level tasks, and we show that with a large enough batch size, the transformer does indeed outperform recurrent models. We also introduce a simple technique to handle feature-guided character-level transduction that further improves performance. With these insights, we achieve state-of-the-art performance on morphological inflection and historical text normalization. We also show that the transformer outperforms a strong baseline on two other character-level transduction tasks: grapheme-to-phoneme conversion and transliteration.

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Do RNN States Encode Abstract Phonological Alternations?
Miikka Silfverberg | Francis Tyers | Garrett Nicolai | Mans Hulden
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Sequence-to-sequence models have delivered impressive results in word formation tasks such as morphological inflection, often learning to model subtle morphophonological details with limited training data. Despite the performance, the opacity of neural models makes it difficult to determine whether complex generalizations are learned, or whether a kind of separate rote memorization of each morphophonological process takes place. To investigate whether complex alternations are simply memorized or whether there is some level of generalization across related sound changes in a sequence-to-sequence model, we perform several experiments on Finnish consonant gradation—a complex set of sound changes triggered in some words by certain suffixes. We find that our models often—though not always—encode 17 different consonant gradation processes in a handful of dimensions in the RNN. We also show that by scaling the activations in these dimensions we can control whether consonant gradation occurs and the direction of the gradation.

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To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings
Sarah Moeller | Ling Liu | Mans Hulden
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Part-of-Speech (POS) tags are routinely included as features in many NLP tasks. However, the importance and usefulness of POS tags needs to be examined as NLP expands to low-resource languages because linguists who provide many annotated resources do not place priority on early identification and tagging of POS. This paper describes an empirical study about the effect that POS tags have on two computational morphological tasks with the Transformer architecture. Each task is tested twice on identical data except for the presence/absence of POS tags, using published data in ten high- to low-resource languages or unpublished linguistic field data in five low-resource languages. We find that the presence or absence of POS tags does not have a significant bearing on performance. In joint segmentation and glossing, the largest average difference is an .09 improvement in F1-scores by removing POS tags. In reinflection, the greatest average difference is 1.2% in accuracy for published data and 5% for unpublished and noisy field data.

2020

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Analogy Models for Neural Word Inflection
Ling Liu | Mans Hulden
Proceedings of the 28th International Conference on Computational Linguistics

Analogy is assumed to be the cognitive mechanism speakers resort to in order to inflect an unknown form of a lexeme based on knowledge of other words in a language. In this process, an analogy is formed between word forms within an inflectional paradigm but also across paradigms. As neural network models for inflection are typically trained only on lemma-target form pairs, we propose three new ways to provide neural models with additional source forms to strengthen analogy-formation, and compare our methods to other approaches in the literature. We show that the proposed methods of providing a Transformer sequence-to-sequence model with additional analogy sources in the input are consistently effective, and improve upon recent state-of-the-art results on 46 languages, particularly in low-resource settings. We also propose a method to combine the analogy-motivated approach with data hallucination or augmentation. We find that the two approaches are complementary to each other and combining the two approaches is especially helpful when the training data is extremely limited.

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UniMorph 3.0: Universal Morphology
Arya D. McCarthy | Christo Kirov | Matteo Grella | Amrit Nidhi | Patrick Xia | Kyle Gorman | Ekaterina Vylomova | Sabrina J. Mielke | Garrett Nicolai | Miikka Silfverberg | Timofey Arkhangelskiy | Nataly Krizhanovsky | Andrew Krizhanovsky | Elena Klyachko | Alexey Sorokin | John Mansfield | Valts Ernštreits | Yuval Pinter | Cassandra L. Jacobs | Ryan Cotterell | Mans Hulden | David Yarowsky
Proceedings of the 12th Language Resources and Evaluation Conference

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological paradigms for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. We have implemented several improvements to the extraction pipeline which creates most of our data, so that it is both more complete and more correct. We have added 66 new languages, as well as new parts of speech for 12 languages. We have also amended the schema in several ways. Finally, we present three new community tools: two to validate data for resource creators, and one to make morphological data available from the command line. UniMorph is based at the Center for Language and Speech Processing (CLSP) at Johns Hopkins University in Baltimore, Maryland. This paper details advances made to the schema, tooling, and dissemination of project resources since the UniMorph 2.0 release described at LREC 2018.

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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Ekaterina Vylomova | Jennifer White | Elizabeth Salesky | Sabrina J. Mielke | Shijie Wu | Edoardo Maria Ponti | Rowan Hall Maudslay | Ran Zmigrod | Josef Valvoda | Svetlana Toldova | Francis Tyers | Elena Klyachko | Ilya Yegorov | Natalia Krizhanovsky | Paula Czarnowska | Irene Nikkarinen | Andrew Krizhanovsky | Tiago Pimentel | Lucas Torroba Hennigen | Christo Kirov | Garrett Nicolai | Adina Williams | Antonios Anastasopoulos | Hilaria Cruz | Eleanor Chodroff | Ryan Cotterell | Miikka Silfverberg | Mans Hulden
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from just one language such as English. The SIGMORPHON 2020 shared task on morphological reinflection aims to investigate systems’ ability to generalize across typologically distinct languages, many of which are low resource. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages. A total of 22 systems (19 neural) from 10 teams were submitted to the task. All four winning systems were neural (two monolingual transformers and two massively multilingual RNN-based models with gated attention). Most teams demonstrate utility of data hallucination and augmentation, ensembles, and multilingual training for low-resource languages. Non-neural learners and manually designed grammars showed competitive and even superior performance on some languages (such as Ingrian, Tajik, Tagalog, Zarma, Lingala), especially with very limited data. Some language families (Afro-Asiatic, Niger-Congo, Turkic) were relatively easy for most systems and achieved over 90% mean accuracy while others were more challenging.

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The SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion
Katharina Kann | Arya D. McCarthy | Garrett Nicolai | Mans Hulden
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

In this paper, we describe the findings of the SIGMORPHON 2020 shared task on unsupervised morphological paradigm completion (SIGMORPHON 2020 Task 2), a novel task in the field of inflectional morphology. Participants were asked to submit systems which take raw text and a list of lemmas as input, and output all inflected forms, i.e., the entire morphological paradigm, of each lemma. In order to simulate a realistic use case, we first released data for 5 development languages. However, systems were officially evaluated on 9 surprise languages, which were only revealed a few days before the submission deadline. We provided a modular baseline system, which is a pipeline of 4 components. 3 teams submitted a total of 7 systems, but, surprisingly, none of the submitted systems was able to improve over the baseline on average over all 9 test languages. Only on 3 languages did a submitted system obtain the best results. This shows that unsupervised morphological paradigm completion is still largely unsolved. We present an analysis here, so that this shared task will ground further research on the topic.

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Leveraging Principal Parts for Morphological Inflection
Ling Liu | Mans Hulden
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

This paper presents the submission by the CU Ling team from the University of Colorado to SIGMORPHON 2020 shared task 0 on morphological inflection. The task is to generate the target inflected word form given a lemma form and a target morphosyntactic description. Our system uses the Transformer architecture. Our overall approach is to treat the morphological inflection task as a paradigm cell filling problem and to design the system to leverage principal parts information for better morphological inflection when the training data is limited. We train one model for each language separately without external data. The overall average performance of our submission ranks the first in both average accuracy and Levenshtein distance from the gold inflection among all submissions including those using external resources.

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Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars
Sarah Beemer | Zak Boston | April Bukoski | Daniel Chen | Princess Dickens | Andrew Gerlach | Torin Hopkins | Parth Anand Jawale | Chris Koski | Akanksha Malhotra | Piyush Mishra | Saliha Muradoglu | Lan Sang | Tyler Short | Sagarika Shreevastava | Elizabeth Spaulding | Testumichi Umada | Beilei Xiang | Changbing Yang | Mans Hulden
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

Sequence-to-sequence models have proven to be highly successful in learning morphological inflection from examples as the series of SIGMORPHON/CoNLL shared tasks have shown. It is usually assumed, however, that a linguist working with inflectional examples could in principle develop a gold standard-level morphological analyzer and generator that would surpass a trained neural network model in accuracy of predictions, but that it may require significant amounts of human labor. In this paper, we discuss an experiment where a group of people with some linguistic training develop 25+ grammars as part of the shared task and weigh the cost/benefit ratio of developing grammars by hand. We also present tools that can help linguists triage difficult complex morphophonological phenomena within a language and hypothesize inflectional class membership. We conclude that a significant development effort by trained linguists to analyze and model morphophonological patterns are required in order to surpass the accuracy of neural models.

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Data Augmentation for Transformer-based G2P
Zach Ryan | Mans Hulden
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

The Transformer model has been shown to outperform other neural seq2seq models in several character-level tasks. It is unclear, however, if the Transformer would benefit as much as other seq2seq models from data augmentation strategies in the low-resource setting. In this paper we explore strategies for data augmentation in the g2p task together with the Transformer model. Our results show that a relatively simple alignment-based strategy of identifying consistent input-output subsequences in grapheme-phoneme data coupled together with a subsequent splicing together of such pieces to generate hallucinated data works well in the low-resource setting, often delivering substantial performance improvement over a standard Transformer model.

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IGT2P: From Interlinear Glossed Texts to Paradigms
Sarah Moeller | Ling Liu | Changbing Yang | Katharina Kann | Mans Hulden
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

An intermediate step in the linguistic analysis of an under-documented language is to find and organize inflected forms that are attested in natural speech. From this data, linguists generate unseen inflected word forms in order to test hypotheses about the language’s inflectional patterns and to complete inflectional paradigm tables. To get the data linguists spend many hours manually creating interlinear glossed texts (IGTs). We introduce a new task that speeds this process and automatically generates new morphological resources for natural language processing systems: IGT-to-paradigms (IGT2P). IGT2P generates entire morphological paradigms from IGT input. We show that existing morphological reinflection models can solve the task with 21% to 64% accuracy, depending on the language. We further find that (i) having a language expert spend only a few hours cleaning the noisy IGT data improves performance by as much as 21 percentage points, and (ii) POS tags, which are generally considered a necessary part of NLP morphological reinflection input, have no effect on the accuracy of the models considered here.

2019

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The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection
Arya D. McCarthy | Ekaterina Vylomova | Shijie Wu | Chaitanya Malaviya | Lawrence Wolf-Sonkin | Garrett Nicolai | Christo Kirov | Miikka Silfverberg | Sabrina J. Mielke | Jeffrey Heinz | Ryan Cotterell | Mans Hulden
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages. The first task evolves past years’ inflection tasks by examining transfer of morphological inflection knowledge from a high-resource language to a low-resource language. This year also presents a new second challenge on lemmatization and morphological feature analysis in context. All submissions featured a neural component and built on either this year’s strong baselines or highly ranked systems from previous years’ shared tasks. Every participating team improved in accuracy over the baselines for the inflection task (though not Levenshtein distance), and every team in the contextual analysis task improved on both state-of-the-art neural and non-neural baselines.

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Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)
Antti Arppe | Jeff Good | Mans Hulden | Jordan Lachler | Alexis Palmer | Lane Schwartz | Miikka Silfverberg
Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

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Improving Low-Resource Morphological Learning with Intermediate Forms from Finite State Transducers
Sarah Moeller | Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

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On the Complexity and Typology of Inflectional Morphological Systems
Ryan Cotterell | Christo Kirov | Mans Hulden | Jason Eisner
Transactions of the Association for Computational Linguistics, Volume 7

We quantify the linguistic complexity of different languages’ morphological systems. We verify that there is a statistically significant empirical trade-off between paradigm size and irregularity: A language’s inflectional paradigms may be either large in size or highly irregular, but never both. We define a new measure of paradigm irregularity based on the conditional entropy of the surface realization of a paradigm— how hard it is to jointly predict all the word forms in a paradigm from the lemma. We estimate irregularity by training a predictive model. Our measurements are taken on large morphological paradigms from 36 typologically diverse languages.

2018

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A Computational Model for the Linguistic Notion of Morphological Paradigm
Miikka Silfverberg | Ling Liu | Mans Hulden
Proceedings of the 27th International Conference on Computational Linguistics

In supervised learning of morphological patterns, the strategy of generalizing inflectional tables into more abstract paradigms through alignment of the longest common subsequence found in an inflection table has been proposed as an efficient method to deduce the inflectional behavior of unseen word forms. In this paper, we extend this notion of morphological ‘paradigm’ from earlier work and provide a formalization that more accurately matches linguist intuitions about what an inflectional paradigm is. Additionally, we propose and evaluate a mechanism for learning full human-readable paradigm specifications from incomplete data—a scenario when we only have access to a few inflected forms for each lexeme, and want to reconstruct the missing inflections as well as generalize and group the witnessed patterns into a model of more abstract paradigmatic behavior of lexemes.

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The Computational Complexity of Distinctive Feature Minimization in Phonology
Hubie Chen | Mans Hulden
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We analyze the complexity of the problem of determining whether a set of phonemes forms a natural class and, if so, that of finding the minimal feature specification for the class. A standard assumption in phonology is that finding a minimal feature specification is an automatic part of acquisition and generalization. We find that the natural class decision problem is tractable (i.e. is in P), while the minimization problem is not; the decision version of the problem which determines whether a natural class can be defined with k features or less is NP-complete. We also show that, empirically, a greedy algorithm for finding minimal feature specifications will sometimes fail, and thus cannot be assumed to be the basis for human performance in solving the problem.

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Proceedings of the CoNLLSIGMORPHON 2018 Shared Task: Universal Morphological Reinflection
Mans Hulden | Ryan Cotterell
Proceedings of the CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection

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The CoNLLSIGMORPHON 2018 Shared Task: Universal Morphological Reinflection
Ryan Cotterell | Christo Kirov | John Sylak-Glassman | Géraldine Walther | Ekaterina Vylomova | Arya D. McCarthy | Katharina Kann | Sabrina J. Mielke | Garrett Nicolai | Miikka Silfverberg | David Yarowsky | Jason Eisner | Mans Hulden
Proceedings of the CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection

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UniMorph 2.0: Universal Morphology
Christo Kirov | Ryan Cotterell | John Sylak-Glassman | Géraldine Walther | Ekaterina Vylomova | Patrick Xia | Manaal Faruqui | Sabrina J. Mielke | Arya McCarthy | Sandra Kübler | David Yarowsky | Jason Eisner | Mans Hulden
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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A Computational Architecture for the Morphology of Upper Tanana
Olga Lovick | Christopher Cox | Miikka Silfverberg | Antti Arppe | Mans Hulden
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Initial Experiments in Data-Driven Morphological Analysis for Finnish
Miikka Silfverberg | Mans Hulden
Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages

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Sound Analogies with Phoneme Embeddings
Miikka P. Silfverberg | Lingshuang Mao | Mans Hulden
Proceedings of the Society for Computation in Linguistics (SCiL) 2018

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A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer
Sarah Moeller | Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages

We experiment with training an encoder-decoder neural model for mimicking the behavior of an existing hand-written finite-state morphological grammar for Arapaho verbs, a polysynthetic language with a highly complex verbal inflection system. After adjusting for ambiguous parses, we find that the system is able to generalize to unseen forms with accuracies of 98.68% (unambiguous verbs) and 92.90% (all verbs).

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Automatic Glossing in a Low-Resource Setting for Language Documentation
Sarah Moeller | Mans Hulden
Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages

Morphological analysis of morphologically rich and low-resource languages is important to both descriptive linguistics and natural language processing. Field documentary efforts usually procure analyzed data in cooperation with native speakers who are capable of providing some level of linguistic information. Manually annotating such data is very expensive and the traditional process is arguably too slow in the face of language endangerment and loss. We report on a case study of learning to automatically gloss a Nakh-Daghestanian language, Lezgi, from a very small amount of seed data. We compare a conditional random field based sequence labeler and a neural encoder-decoder model and show that a nearly 0.9 F1-score on labeled accuracy of morphemes can be achieved with 3,000 words of transcribed oral text. Errors are mostly limited to morphemes with high allomorphy. These results are potentially useful for developing rapid annotation and fieldwork tools to support documentation of morphologically rich, endangered languages.

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Phonological Features for Morphological Inflection
Adam Wiemerslage | Miikka Silfverberg | Mans Hulden
Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology

Modeling morphological inflection is an important task in Natural Language Processing. In contrast to earlier work that has largely used orthographic representations, we experiment with this task in a phonetic character space, representing inputs as either IPA segments or bundles of phonological distinctive features. We show that both of these inputs, somewhat counterintuitively, achieve similar accuracies on morphological inflection, slightly lower than orthographic models. We conclude that providing detailed phonological representations is largely redundant when compared to IPA segments, and that articulatory distinctions relevant for word inflection are already latently present in the distributional properties of many graphemic writing systems.

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Marrying Universal Dependencies and Universal Morphology
Arya D. McCarthy | Miikka Silfverberg | Ryan Cotterell | Mans Hulden | David Yarowsky
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages—UD at the token level and UniMorph at the type level. As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata. With compatibility of tags, each project’s annotations could be used to validate the other’s. Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation. To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema. We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13% recall. We also note incompatibilities due to paucity of data on either side. Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects.

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An Encoder-Decoder Approach to the Paradigm Cell Filling Problem
Miikka Silfverberg | Mans Hulden
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The Paradigm Cell Filling Problem in morphology asks to complete word inflection tables from partial ones. We implement novel neural models for this task, evaluating them on 18 data sets in 8 languages, showing performance that is comparable with previous work with far less training data. We also publish a new dataset for this task and code implementing the system described in this paper.

2017

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Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages
Antti Arppe | Jeff Good | Mans Hulden | Jordan Lachler | Alexis Palmer | Lane Schwartz
Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages

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Creating lexical resources for polysynthetic languages—the case of Arapaho
Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages

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Automatic Morpheme Segmentation and Labeling in Universal Dependencies Resources
Miikka Silfverberg | Mans Hulden
Proceedings of the NoDaLiDa 2017 Workshop on Universal Dependencies (UDW 2017)

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Evaluation of Finite State Morphological Analyzers Based on Paradigm Extraction from Wiktionary
Ling Liu | Mans Hulden
Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing (FSMNLP 2017)

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Weakly supervised learning of allomorphy
Miikka Silfverberg | Mans Hulden
Proceedings of the First Workshop on Subword and Character Level Models in NLP

Most NLP resources that offer annotations at the word segment level provide morphological annotation that includes features indicating tense, aspect, modality, gender, case, and other inflectional information. Such information is rarely aligned to the relevant parts of the words—i.e. the allomorphs, as such annotation would be very costly. These unaligned weak labelings are commonly provided by annotated NLP corpora such as treebanks in various languages. Although they lack alignment information, the presence/absence of labels at the word level is also consistent with the amount of supervision assumed to be provided to L1 and L2 learners. In this paper, we explore several methods to learn this latent alignment between parts of word forms and the grammatical information provided. All the methods under investigation favor hypotheses regarding allomorphs of morphemes that re-use a small inventory, i.e. implicitly minimize the number of allomorphs that a morpheme can be realized as. We show that the provided information offers a significant advantage for both word segmentation and the learning of allomorphy.

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A phoneme clustering algorithm based on the obligatory contour principle
Mans Hulden
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

This paper explores a divisive hierarchical clustering algorithm based on the well-known Obligatory Contour Principle in phonology. The purpose is twofold: to see if such an algorithm could be used for unsupervised classification of phonemes or graphemes in corpora, and to investigate whether this purported universal constraint really holds for several classes of phonological distinctive features. The algorithm achieves very high accuracies in an unsupervised setting of inferring a consonant-vowel distinction, and also has a strong tendency to detect coronal phonemes in an unsupervised fashion. Remaining classes, however, do not correspond as neatly to phonological distinctive feature splits. While the results offer only mixed support for a universal Obligatory Contour Principle, the algorithm can be very useful for many NLP tasks due to the high accuracy in revealing consonant/vowel/coronal distinctions.

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Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection
Mans Hulden
Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection

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CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages
Ryan Cotterell | Christo Kirov | John Sylak-Glassman | Géraldine Walther | Ekaterina Vylomova | Patrick Xia | Manaal Faruqui | Sandra Kübler | David Yarowsky | Jason Eisner | Mans Hulden
Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection

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A Comparison of Feature-Based and Neural Scansion of Poetry
Manex Agirrezabal | Iñaki Alegria | Mans Hulden
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

Automatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science. When the language to be analyzed is known, rule-based systems or data-driven methods can be used. In this paper, we analyze poetic rhythm in English and Spanish. We show that the representations of data learned from character-based neural models are more informative than the ones from hand-crafted features, and that a Bi-LSTM+CRF-model produces state-of-the art accuracy on scansion of poetry in two languages. Results also show that the information about whole word structure, and not just independent syllables, is highly informative for performing scansion.

2016

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Evaluating the Noisy Channel Model for the Normalization of Historical Texts: Basque, Spanish and Slovene
Izaskun Etxeberria | Iñaki Alegria | Larraitz Uria | Mans Hulden
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents a method for the normalization of historical texts using a combination of weighted finite-state transducers and language models. We have extended our previous work on the normalization of dialectal texts and tested the method against a 17th century literary work in Basque. This preprocessed corpus is made available in the LREC repository. The performance of this method for learning relations between historical and contemporary word forms is evaluated against resources in three languages. The method we present learns to map phonological changes using a noisy channel model. The model is based on techniques commonly used for phonological inference and producing Grapheme-to-Grapheme conversion systems encoded as weighted transducers and produces F-scores above 80% in the task for Basque. A wider evaluation shows that the approach performs equally well with all the languages in our evaluation suite: Basque, Spanish and Slovene. A comparison against other methods that address the same task is also provided.

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Deriving Morphological Analyzers from Example Inflections
Markus Forsberg | Mans Hulden
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents a semi-automatic method to derive morphological analyzers from a limited number of example inflections suitable for languages with alphabetic writing systems. The system we present learns the inflectional behavior of morphological paradigms from examples and converts the learned paradigms into a finite-state transducer that is able to map inflected forms of previously unseen words into lemmas and corresponding morphosyntactic descriptions. We evaluate the system when provided with inflection tables for several languages collected from the Wiktionary.

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Morphological Analysis of Sahidic Coptic for Automatic Glossing
Daniel Smith | Mans Hulden
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We report on the implementation of a morphological analyzer for the Sahidic dialect of Coptic, a now extinct Afro-Asiatic language. The system is developed in the finite-state paradigm. The main purpose of the project is provide a method by which scholars and linguists can semi-automatically gloss extant texts written in Sahidic. Since a complete lexicon containing all attested forms in different manuscripts requires significant expertise in Coptic spanning almost 1,000 years, we have equipped the analyzer with a core lexicon and extended it with a “guesser” ability to capture out-of-vocabulary items in any inflection. We also suggest an ASCII transliteration for the language. A brief evaluation is provided.

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The SIGMORPHON 2016 Shared Task—Morphological Reinflection
Ryan Cotterell | Christo Kirov | John Sylak-Glassman | David Yarowsky | Jason Eisner | Mans Hulden
Proceedings of the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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Combining Phonology and Morphology for the Normalization of Historical Texts
Izaskun Etxeberria | Iñaki Alegria | Larraitz Uria | Mans Hulden
Proceedings of the 10th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities

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Learning Transducer Models for Morphological Analysis from Example Inflections
Markus Forsberg | Mans Hulden
Proceedings of the SIGFSM Workshop on Statistical NLP and Weighted Automata

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Machine Learning for Metrical Analysis of English Poetry
Manex Agirrezabal | Iñaki Alegria | Mans Hulden
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In this work we tackle the challenge of identifying rhythmic patterns in poetry written in English. Although poetry is a literary form that makes use standard meters usually repeated among different authors, we will see in this paper how performing such analyses is a difficult task in machine learning due to the unexpected deviations from such standard patterns. After breaking down some examples of classical poetry, we apply a number of NLP techniques for the scansion of poetry, training and testing our systems against a human-annotated corpus. With these experiments, our purpose is establish a baseline of automatic scansion of poetry using NLP tools in a straightforward manner and to raise awareness of the difficulties of this task.

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How Regular is Japanese Loanword Adaptation? A Computational Study
Lingshuang Mao | Mans Hulden
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

The modifications that foreign loanwords undergo when adapted into Japanese have been the subject of much study in linguistics. The scholarly interest of the topic can be attributed to the fact that Japanese loanwords undergo a complex series of phonological adaptations, something which has been puzzling scholars for decades. While previous studies of Japanese loanword accommodation have focused on specific phonological phenomena of limited scope, the current study leverages computational methods to provide a more complete description of all the sound changes that occur when adopting English words into Japanese. To investigate this, we have developed a parallel corpus of 250 English transcriptions and their respective Japanese equivalents. These words were then used to develop a wide-coverage finite state transducer based phonological grammar that mimics the behavior of the Japanese adaption process. By developing rules with the goal of accounting completely for a large number of borrowing and analyzing forms mistakenly generated by the system, we discovered an internal inconsistency inside the loanword phonology of the Japanese language, something arguably underestimated by previous studies. The result of the investigation suggests that there are multiple ‘dimensions’ that shape the output form of the current Japanese loanwords. These dimensions include orthography, phonetics, and historical changes.

2015

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Grammar Design with Multi-tape Automata and Composition
Mans Hulden
Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 (FSMNLP 2015 Düsseldorf)

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Paradigm classification in supervised learning of morphology
Malin Ahlberg | Markus Forsberg | Mans Hulden
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Semi-supervised learning of morphological paradigms and lexicons
Mans Hulden | Markus Forsberg | Malin Ahlberg
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Generalizing Inflection Tables into Paradigms with Finite State Operations
Mans Hulden
Proceedings of the 2014 Joint Meeting of SIGMORPHON and SIGFSM

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Why Implementation Matters: Evaluation of an Open-source Constraint Grammar Parser
Dávid Márk Nemeskey | Francis Tyers | Mans Hulden
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Computer-aided morphology expansion for Old Swedish
Yvonne Adesam | Malin Ahlberg | Peter Andersson | Gerlof Bouma | Markus Forsberg | Mans Hulden
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper we describe and evaluate a tool for paradigm induction and lexicon extraction that has been applied to Old Swedish. The tool is semi-supervised and uses a small seed lexicon and unannotated corpora to derive full inflection tables for input lemmata. In the work presented here, the tool has been modified to deal with the rich spelling variation found in Old Swedish texts. We also present some initial experiments, which are the first steps towards creating a large-scale morphology for Old Swedish.

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ACTIV-ES: a comparable, cross-dialect corpus of ‘everyday’ Spanish from Argentina, Mexico, and Spain
Jerid Francom | Mans Hulden | Adam Ussishkin
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Corpus resources for Spanish have proved invaluable for a number of applications in a wide variety of fields. However, a majority of resources are based on formal, written language and/or are not built to model language variation between varieties of the Spanish language, despite the fact that most language in ‘everyday’ use is informal/ dialogue-based and shows rich regional variation. This paper outlines the development and evaluation of the ACTIV-ES corpus, a first-step to produce a comparable, cross-dialect corpus representative of the ‘everyday’ language of various regions of the Spanish-speaking world.

2013

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ZeuScansion: a tool for scansion of English poetry
Manex Agirrezabal | Bertol Arrieta | Aitzol Astigarraga | Mans Hulden
Proceedings of the 11th International Conference on Finite State Methods and Natural Language Processing

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POS-Tag Based Poetry Generation with WordNet
Manex Agirrezabal | Bertol Arrieta | Aitzol Astigarraga | Mans Hulden
Proceedings of the 14th European Workshop on Natural Language Generation

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Finite State Applications with Javascript
Mans Hulden | Miikka Silfverberg | Jerid Francom
Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013)

2012

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BAD: An Assistant tool for making verses in Basque
Manex Agirrezabal | Iñaki Alegria | Bertol Arrieta | Mans Hulden
Proceedings of the 6th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities

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Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing
Iñaki Alegria | Mans Hulden
Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing

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Practical Finite State Optimality Theory
Dale Gerdemann | Mans Hulden
Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing

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Finite-State Technology in a Verse-Making Tool
Manex Agirrezabal | Iñaki Alegria | Bertol Arrieta | Mans Hulden
Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing

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Developing an Open-Source FST Grammar for Verb Chain Transfer in a Spanish-Basque MT System
Aingeru Mayor | Mans Hulden | Gorka Labaka
Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing

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Conversion of Procedural Morphologies to Finite-State Morphologies: A Case Study of Arabic
Mans Hulden | Younes Samih
Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing

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Boosting statistical tagger accuracy with simple rule-based grammars
Mans Hulden | Jerid Francom
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We report on several experiments on combining a rule-based tagger and a trigram tagger for Spanish. The results show that one can boost the accuracy of the best performing n-gram taggers by quickly developing a rough rule-based grammar to complement the statistically induced one and then combining the output of the two. The specific method of combination is crucial for achieving good results. The method provides particularly large gains in accuracy when only a small amount of tagged data is available for training a HMM, as may be the case for lesser-resourced and minority languages.

2011

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Learning word-level dialectal variation as phonological replacement rules using a limited parallel corpus
Mans Hulden | Iñaki Alegria | Izaskun Etxeberria | Montse Maritxalar
Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties

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Constraint Grammar Parsing with Left and Right Sequential Finite Transducers
Mans Hulden
Proceedings of the 9th International Workshop on Finite State Methods and Natural Language Processing

2009

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Foma: a Finite-State Compiler and Library
Mans Hulden
Proceedings of the Demonstrations Session at EACL 2009

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Revisiting Multi-Tape Automata for Semitic Morphological Analysis and Generation
Mans Hulden
Proceedings of the EACL 2009 Workshop on Computational Approaches to Semitic Languages

2008

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Parallel Multi-Theory Annotations of Syntactic Structure
Jerid Francom | Mans Hulden
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We present an approach to creating a treebank of sentences using multiple notations or linguistic theories simultaneously. We illustrate the method by annotating sentences from the Penn Treebank II in three different theories in parallel: the original PTB notation, a Functional Dependency Grammar notation, and a Government and Binding style notation. Sentences annotated with all of these theories are represented in XML as a directed acyclic graph where nodes and edges may carry extra information depending on the theory encoded.
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