Mans Hulden

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29ranked-venue papers

5as first author

6since 2021 · last 2022

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 28 · 4 first-author · 6 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorTheory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 5 papers		Information extraction and text analysis · 60% Efficient and distributed learning · 18% Language models and text generation · 18%
Databases, data mining, and information retrieval 1 paper		Information retrieval · 100%

Topics — the 8 heaviest of 10, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Natural language and speech › Information extraction and text analysis morphological analysis	1.3	3	2021	To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings · ACL/IJCNLP (1) 2021 IGT2P: From Interlinear Glossed Texts to Paradigms · EMNLP (1) 2020 An Encoder-Decoder Approach to the Paradigm Cell Filling Problem · EMNLP 2018
Natural language and speech › Language models and text generation low-resource language processing	0.7	2	2022	Eeny, meeny, miny, moe. How to choose data for morphological inflection · EMNLP 2022 To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings · ACL/IJCNLP (1) 2021
Machine learning › Efficient and distributed learning data selection	0.6	1	2022	Eeny, meeny, miny, moe. How to choose data for morphological inflection · EMNLP 2022
Natural language and speech › Information extraction and text analysis › computational morphology morphological inflection	0.6	1	2022	Eeny, meeny, miny, moe. How to choose data for morphological inflection · EMNLP 2022
Natural language and speech › Information extraction and text analysis › sequence labeling part-of-speech tagging	0.5	1	2021	To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings · ACL/IJCNLP (1) 2021
Machine learning › Efficient and distributed learning data-efficient learning	0.2	1	2022	Eeny, meeny, miny, moe. How to choose data for morphological inflection · EMNLP 2022
Machine learning › Deep learning architectures and training encoder-decoder architecture	0.1	1	2018	An Encoder-Decoder Approach to the Paradigm Cell Filling Problem · EMNLP 2018
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › density estimation kernel density estimation	0.1	1	2015	Kernel Density Estimation for Text-Based Geolocation · AAAI 2015

Methods — techniques the papers use, named apart from their topics

part-of-speech tagging · 0.9transformer · 0.6entropy-based sampling · 0.6confidence-based sampling · 0.6active learning · 0.6morphological learning · 0.5kernel density estimation · 0.4morphological reinflection · 0.4encoder-decoder neural network · 0.3

Year	Publication	Venue	Position
2022	Eeny, meeny, miny, moe. How to choose data for morphological inflection abstract Data scarcity is a widespread problem in numerous natural language processing (NLP) tasks for low-resource languages.Within morphology, the labour-intensive work of tagging/glossing data is a serious bottleneck for both NLP and language documentation.Active learning (AL) aims to reduce the cost of data annotation by selecting data that is most informative for improving the model.In this paper, we explore four sampling strategies for the task of morphological inflection using a Transformer model: a pair of oracle experiments where data is chosen based on whether the model already can or cannot inflect the test forms correctly, as well as strategies based on high/low model confidence, entropy, as well as random selection.We investigate the robustness of each strategy across 30 typologically diverse languages.We also perform a more in-depth case study of Natügu.Our results show a clear benefit to selecting data based on model confidence and entropy.Unsurprisingly, the oracle experiment, where only incorrectly handled forms are chosen for further training, which is presented as a proxy for linguist/language consultant feedback, shows the most improvement.This is followed closely by choosing low-confidence and high-entropy predictions.We also show that despite the conventional wisdom of larger data sets yielding better accuracy, introducing more instances of high-confidence or low-entropy forms, or forms that the model can already inflect correctly, can reduce model performance. Saliha Muradoglu, Mans Hulden	EMNLP	2
2022	UniMorph 4.0: Universal Morphology abstract The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables 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. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet. Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieras, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane 0002, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóga, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer C. White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo Maria Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar 0002, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Tucker Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova	LREC	89
2022	My Case, For an Adposition: Lexical Polysemy of Adpositions and Case Markers in Finnish and Latin abstract Adpositions and case markers contain a high degree of polysemy and participate in unique semantic role configurations. We present a novel application of the SNACS supersense hierarchy to Finnish and Latin data by manually annotating adposition and case marker tokens in Finnish and Latin translations of Chapters IV-V of Le Petit Prince (The Little Prince). We evaluate the computational validity of the semantic role annotation categories by grouping raw, contextualized Multilingual BERT embeddings using k-means clustering. Mans Hulden	LREC	2
2021	To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings abstract 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). 2021. Sarah R. Moeller, Mans Hulden	ACL/IJCNLP (1)	3
2021	Applying the Transformer to Character-level Transduction abstract The transformer (Vaswani et al., 2017) 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 sequenceto-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. Ryan Cotterell, Mans Hulden	EACL	3
2021	Do RNN States Encode Abstract Phonological Alternations? abstract 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. 2021. Miikka Silfverberg, Francis M. Tyers, Garrett Nicolai, Mans Hulden	NAACL-HLT	4
2020	Analogy Models for Neural Word Inflection abstract 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. Mans Hulden	COLING	2
2020	IGT2P: From Interlinear Glossed Texts to Paradigms abstract 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: IGTto-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. Sarah R. Moeller, Changbing Yang, Katharina Kann, Mans Hulden	EMNLP (1)	5
2020	UniMorph 3.0: Universal Morphology abstract 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. Arya McCarthy, Christo Kirov, Matteo Grella, Amrit Nidhi, Patrick Xia 0002, Kyle Gorman, Ekaterina Vylomova, Sabrina J. Mielke, Garrett Nicolai, Miikka Silfverberg, Timofey Arkhangelskiy, Nataly Krizhanovsky, Andrew Krizhanovsky, Elena Klyachko, Alexey Sorokin, John Mansfield, Valts Ernstreits, Yuval Pinter, Cassandra L. Jacobs, Ryan Cotterell, Mans Hulden, David Yarowsky	LREC	21
2019	On the Complexity and Typology of Inflectional Morphological Systems abstract 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. Ryan Cotterell, Christo Kirov, Mans Hulden, Jason Eisner	Trans. Assoc. Comput. Linguistics	3
2018	A Computational Model for the Linguistic Notion of Morphological Paradigm abstract 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. Miikka Silfverberg, Mans Hulden	COLING	3
2018	An Encoder-Decoder Approach to the Paradigm Cell Filling Problem abstract 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.1 Miikka Silfverberg, Mans Hulden	EMNLP	2
2018	UniMorph 2.0: Universal Morphology Christo Kirov, Ryan Cotterell, John Sylak-Glassman, Géraldine Walther, Ekaterina Vylomova, Patrick Xia 0002, Manaal Faruqui, Sabrina J. Mielke, Arya McCarthy, Sandra Kübler, David Yarowsky, Jason Eisner, Mans Hulden	LREC	13
2018	A Computational Architecture for the Morphology of Upper Tanana Olga Lovick, Christopher Cox, Miikka Silfverberg, Antti Arppe, Mans Hulden	LREC	5
2017	A phoneme clustering algorithm based on the obligatory contour principle abstract This paper explores a divisive hierarchical clustering algorithm based on the wellknown 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 consonantvowel 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. Mans Hulden	CoNLL	1
2016	Machine Learning for Metrical Analysis of English Poetry abstract 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. Manex Agirrezabal, Iñaki Alegria, Mans Hulden	COLING	3
2016	How Regular is Japanese Loanword Adaptation? A Computational Study abstract 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. Lingshuang Mao, Mans Hulden	COLING	2
2016	Evaluating the Noisy Channel Model for the Normalization of Historical Texts: Basque, Spanish and Slovene Izaskun Etxeberria, Iñaki Alegria, Larraitz Uria, Mans Hulden	LREC	4
2016	Deriving Morphological Analyzers from Example Inflections Markus Forsberg, Mans Hulden	LREC	2
2016	Morphological Analysis of Sahidic Coptic for Automatic Glossing Mans Hulden	LREC	2
2015	Kernel Density Estimation for Text-Based Geolocation abstract Text-based geolocation classifiers often operate with a grid-based view of the world. Predicting document location of origin based on text content on a geodesic grid is computationally attractive since many standard methods for supervised document classification carry over unchanged to geolocation in the form of predicting a most probable grid cell for a document. However, the grid-based approach suffers from sparse data problems if one wants to improve classification accuracy by moving to smaller cell sizes. In this paper we investigate an enhancement of common methods for determining the geographic point of origin of a text document by kernel density estimation. For geolocation of tweets we obtain a improvements upon non-kernel methods on datasets of U.S. and global Twitter content. Mans Hulden, Miikka Silfverberg, Jerid Francom	AAAI	1
2015	Paradigm classification in supervised learning of morphology abstract Supervised morphological paradigm learning by identifying and aligning the longest common subsequence found in inflection tables has recently been proposed as a simple yet competitive way to induce morphological patterns.We combine this non-probabilistic strategy of inflection table generalization with a discriminative classifier to permit the reconstruction of complete inflection tables of unseen words.Our system learns morphological paradigms from labeled examples of inflection patterns (inflection tables) and then produces inflection tables from unseen lemmas or base forms.We evaluate the approach on datasets covering 11 different languages and show that this approach results in consistently higher accuracies vis-à-vis other methods on the same task, thus indicating that the general method is a viable approach to quickly creating highaccuracy morphological resources. Malin Ahlberg, Markus Forsberg, Mans Hulden	HLT-NAACL	3
2015	From Two-Way to One-Way Finite Automata - Three Regular Expression-Based Methods Mans Hulden	CIAA	1
2014	Why Implementation Matters: Evaluation of an Open-source Constraint Grammar Parser Dávid Márk Nemeskey, Francis M. Tyers, Mans Hulden	COLING	3
2014	Semi-supervised learning of morphological paradigms and lexicons abstract We present a semi-supervised approach to the problem of paradigm induction from inflection tables.Our system extracts generalizations from inflection tables, representing the resulting paradigms in an abstract form.The process is intended to be language-independent, and to provide human-readable generalizations of paradigms.The tools we provide can be used by linguists for the rapid creation of lexical resources.We evaluate the system through an inflection table reconstruction task using Wiktionary data for German, Spanish, and Finnish.With no additional corpus information available, the evaluation yields per word form accuracy scores on inflecting unseen base forms in different languages ranging from 87.81% (German nouns) to 99.52% (Spanish verbs); with additional unlabeled text corpora available for training the scores range from 91.81% (German nouns) to 99.58% (Spanish verbs).We separately evaluate the system in a simulated task of Swedish lexicon creation, and show that on the basis of a small number of inflection tables, the system can accurately collect from a list of noun forms a lexicon with inflection information ranging from 100.0% correct (collect 100 words), to 96.4% correct (collect 1000 words). Mans Hulden, Markus Forsberg, Malin Ahlberg	EACL	1
2014	Computer-aided morphology expansion for Old Swedish Yvonne Adesam, Malin Ahlberg, Peter Andersson, Gerlof Bouma, Markus Forsberg, Mans Hulden	LREC	6
2014	ACTIV-ES: a comparable, cross-dialect corpus of 'everyday' Spanish from Argentina, Mexico, and Spain Jerid Francom, Mans Hulden, Adam Ussishkin	LREC	2
2012	Boosting statistical tagger accuracy with simple rule-based grammars Mans Hulden, Jerid Francom	LREC	1
2008	Parallel Multi-Theory Annotations of Syntactic Structure Jerid Francom, Mans Hulden	LREC	2