Christo Kirov


Spelling convention sensitivity in neural language models
Elizabeth Nielsen | Christo Kirov | Brian Roark
Findings of the Association for Computational Linguistics: EACL 2023

We examine whether large neural language models, trained on very large collections of varied English text, learn the potentially long-distance dependency of British versus American spelling conventions, i.e., whether spelling is consistently one or the other within model-generated strings. In contrast to long-distance dependencies in non-surface underlying structure (e.g., syntax), spelling consistency is easier to measure both in LMs and the text corpora used to train them, which can provide additional insight into certain observed model behaviors. Using a set of probe words unique to either British or American English, we first establish that training corpora exhibit substantial (though not total) consistency. A large T5 language model does appear to internalize this consistency, though only with respect to observed lexical items (not nonce words with British/American spelling patterns). We further experiment with correcting for biases in the training data by fine-tuning T5 on synthetic data that has been debiased, and find that finetuned T5 remains only somewhat sensitive to spelling consistency. Further experiments show GPT2 to be similarly limited.


Mockingbird at the SIGTYP 2022 Shared Task: Two Types of Models for the Prediction of Cognate Reflexes
Christo Kirov | Richard Sproat | Alexander Gutkin
Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

The SIGTYP 2022 shared task concerns the problem of word reflex generation in a target language, given cognate words from a subset of related languages. We present two systems to tackle this problem, covering two very different modeling approaches. The first model extends transformer-based encoder-decoder sequence-to-sequence modeling, by encoding all available input cognates in parallel, and having the decoder attend to the resulting joint representation during inference. The second approach takes inspiration from the field of image restoration, where models are tasked with recovering pixels in an image that have been masked out. For reflex generation, the missing reflexes are treated as “masked pixels” in an “image” which is a representation of an entire cognate set across a language family. As in the image restoration case, cognate restoration is performed with a convolutional network.


Structured abbreviation expansion in context
Kyle Gorman | Christo Kirov | Brian Roark | Richard Sproat
Findings of the Association for Computational Linguistics: EMNLP 2021

Ad hoc abbreviations are commonly found in informal communication channels that favor shorter messages. We consider the task of reversing these abbreviations in context to recover normalized, expanded versions of abbreviated messages. The problem is related to, but distinct from, spelling correction, as ad hoc abbreviations are intentional and can involve more substantial differences from the original words. Ad hoc abbreviations are also productively generated on-the-fly, so they cannot be resolved solely by dictionary lookup. We generate a large, open-source data set of ad hoc abbreviations. This data is used to study abbreviation strategies and to develop two strong baselines for abbreviation expansion.


Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset
Brian Roark | Lawrence Wolf-Sonkin | Christo Kirov | Sabrina J. Mielke | Cibu Johny | Isin Demirsahin | Keith Hall
Proceedings of the Twelfth Language Resources and Evaluation Conference

This paper describes the Dakshina dataset, a new resource consisting of text in both the Latin and native scripts for 12 South Asian languages. The dataset includes, for each language: 1) native script Wikipedia text; 2) a romanization lexicon; and 3) full sentence parallel data in both a native script of the language and the basic Latin alphabet. We document the methods used for preparation and selection of the Wikipedia text in each language; collection of attested romanizations for sampled lexicons; and manual romanization of held-out sentences from the native script collections. We additionally provide baseline results on several tasks made possible by the dataset, including single word transliteration, full sentence transliteration, and language modeling of native script and romanized text.

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 Twelfth 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.


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.

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.


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)

Improving Low Resource Machine Translation using Morphological Glosses (Non-archival Extended Abstract)
Steven Shearing | Christo Kirov | Huda Khayrallah | David Yarowsky
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

Unsupervised Disambiguation of Syncretism in Inflected Lexicons
Ryan Cotterell | Christo Kirov | Sabrina J. Mielke | Jason Eisner
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Lexical ambiguity makes it difficult to compute useful statistics of a corpus. A given word form might represent any of several morphological feature bundles. One can, however, use unsupervised learning (as in EM) to fit a model that probabilistically disambiguates word forms. We present such an approach, which employs a neural network to smoothly model a prior distribution over feature bundles (even rare ones). Although this basic model does not consider a token’s context, that very property allows it to operate on a simple list of unigram type counts, partitioning each count among different analyses of that unigram. We discuss evaluation metrics for this novel task and report results on 5 languages.

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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

Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate
Christo Kirov | Ryan Cotterell
Transactions of the Association for Computational Linguistics, Volume 6

Can advances in NLP help advance cognitive modeling? We examine the role of artificial neural networks, the current state of the art in many common NLP tasks, by returning to a classic case study. In 1986, Rumelhart and McClelland famously introduced a neural architecture that learned to transduce English verb stems to their past tense forms. Shortly thereafter in 1988, Pinker and Prince presented a comprehensive rebuttal of many of Rumelhart and McClelland’s claims. Much of the force of their attack centered on the empirical inadequacy of the Rumelhart and McClelland model. Today, however, that model is severely outmoded. We show that the Encoder-Decoder network architectures used in modern NLP systems obviate most of Pinker and Prince’s criticisms without requiring any simplification of the past tense mapping problem. We suggest that the empirical performance of modern networks warrants a reexamination of their utility in linguistic and cognitive modeling.


A Rich Morphological Tagger for English: Exploring the Cross-Linguistic Tradeoff Between Morphology and Syntax
Christo Kirov | John Sylak-Glassman | Rebecca Knowles | Ryan Cotterell | Matt Post
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

A traditional claim in linguistics is that all human languages are equally expressive—able to convey the same wide range of meanings. Morphologically rich languages, such as Czech, rely on overt inflectional and derivational morphology to convey many semantic distinctions. Languages with comparatively limited morphology, such as English, should be able to accomplish the same using a combination of syntactic and contextual cues. We capitalize on this idea by training a tagger for English that uses syntactic features obtained by automatic parsing to recover complex morphological tags projected from Czech. The high accuracy of the resulting model provides quantitative confirmation of the underlying linguistic hypothesis of equal expressivity, and bodes well for future improvements in downstream HLT tasks including machine translation.

Neural Graphical Models over Strings for Principal Parts Morphological Paradigm Completion
Ryan Cotterell | John Sylak-Glassman | Christo Kirov
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Many of the world’s languages contain an abundance of inflected forms for each lexeme. A critical task in processing such languages is predicting these inflected forms. We develop a novel statistical model for the problem, drawing on graphical modeling techniques and recent advances in deep learning. We derive a Metropolis-Hastings algorithm to jointly decode the model. Our Bayesian network draws inspiration from principal parts morphological analysis. We demonstrate improvements on 5 languages.

Paradigm Completion for Derivational Morphology
Ryan Cotterell | Ekaterina Vylomova | Huda Khayrallah | Christo Kirov | David Yarowsky
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models adapted from the inflection task are able to learn the range of derivation patterns, and outperform a non-neural baseline by 16.4%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.

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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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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

Remote Elicitation of Inflectional Paradigms to Seed Morphological Analysis in Low-Resource Languages
John Sylak-Glassman | Christo Kirov | David Yarowsky
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Structured, complete inflectional paradigm data exists for very few of the world’s languages, but is crucial to training morphological analysis tools. We present methods inspired by linguistic fieldwork for gathering inflectional paradigm data in a machine-readable, interoperable format from remotely-located speakers of any language. Informants are tasked with completing language-specific paradigm elicitation templates. Templates are constructed by linguists using grammatical reference materials to ensure completeness. Each cell in a template is associated with contextual prompts designed to help informants with varying levels of linguistic expertise (from professional translators to untrained native speakers) provide the desired inflected form. To facilitate downstream use in interoperable NLP/HLT applications, each cell is also associated with a language-independent machine-readable set of morphological tags from the UniMorph Schema. This data is useful for seeding morphological analysis and generation software, particularly when the data is representative of the range of surface morphological variation in the language. At present, we have obtained 792 lemmas and 25,056 inflected forms from 15 languages.

Very-large Scale Parsing and Normalization of Wiktionary Morphological Paradigms
Christo Kirov | John Sylak-Glassman | Roger Que | David Yarowsky
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Wiktionary is a large-scale resource for cross-lingual lexical information with great potential utility for machine translation (MT) and many other NLP tasks, especially automatic morphological analysis and generation. However, it is designed primarily for human viewing rather than machine readability, and presents numerous challenges for generalized parsing and extraction due to a lack of standardized formatting and grammatical descriptor definitions. This paper describes a large-scale effort to automatically extract and standardize the data in Wiktionary and make it available for use by the NLP research community. The methodological innovations include a multidimensional table parsing algorithm, a cross-lexeme, token-frequency-based method of separating inflectional form data from grammatical descriptors, the normalization of grammatical descriptors to a unified annotation scheme that accounts for cross-linguistic diversity, and a verification and correction process that exploits within-language, cross-lexeme table format consistency to minimize human effort. The effort described here resulted in the extraction of a uniquely large normalized resource of nearly 1,000,000 inflectional paradigms across 350 languages. Evaluation shows that even though the data is extracted using a language-independent approach, it is comparable in quantity and quality to data extracted using hand-tuned, language-specific approaches.


A Language-Independent Feature Schema for Inflectional Morphology
John Sylak-Glassman | Christo Kirov | David Yarowsky | Roger Que
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)