Papers by Christo Kirov
Spelling convention sensitivity in neural language models (2023.findings-eacl)
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| Challenge: | Various long-distance dependencies have been investigated using neural language models. |
| Approach: | They examine whether large neural language models learn the long-distance dependency of British versus American spelling conventions . a large T5 language model does internalize consistency, but only with respect to observed lexical items . |
| Outcome: | The proposed model internalizes consistency with the training corpora, but only with respect to observed lexical items. |
Unsupervised Disambiguation of Syncretism in Inflected Lexicons (N18-2)
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| Challenge: | Lexical ambiguity makes it difficult to compute useful statistics of a corpus. |
| Approach: | They propose a neural network-based model that fits a prior distribution over feature bundles to a list of unigram type counts and partitions each count among different analyses of that unigrammer. |
| Outcome: | The proposed model is based on a list of unigram type counts and partitions each count among different analyses of that unigrammer. |
Improving Informally Romanized Language Identification (2025.emnlp-main)
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| Challenge: | Latin script is often used to informally write languages with non-Latin native scripts, resulting in high spelling variability. |
| Approach: | They propose to improve methods used to synthesize training sets to incorporate natural spelling variations into training sets. |
| Outcome: | The proposed method improves test F1 from the reported 74.7% (using a pretrained neural model) to 85.4% (using the linear classifier trained solely on synthetic data). |
Structured abbreviation expansion in context (2021.findings-emnlp)
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| Challenge: | Ad hoc abbreviations are commonly found in informal communication channels that favor shorter messages. |
| Approach: | They propose to reverse ad hoc abbreviations in context to recover normalized, expanded versions of abbrevated messages. |
| Outcome: | The proposed method can recover normalized, expanded abbreviations from text . it is similar to spelling correction, but requires more extensive work . |
Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset (2020.lrec-1)
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Brian Roark, Lawrence Wolf-Sonkin, Christo Kirov, Sabrina J. Mielke, Cibu Johny, Isin Demirsahin, Keith Hall
| Challenge: | a new resource is available for 12 South Asian languages that use the Latin script for text entry . the Latin-script system is not widely used in South Asian language writing, despite the Latin alphabet . |
| Approach: | They describe the Dakshina dataset, a new resource consisting of text in both the Latin and native scripts for 12 South Asian languages. |
| Outcome: | The Dakshina dataset includes text in both the Latin and native scripts for 12 languages . the authors provide baseline results on several tasks made possible by the dataset . |
UniMorph 2.0: Universal Morphology (L18-1)
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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
| Challenge: | The Universal Morphology project is a collaborative effort to improve how NLP handles complex morphology across the world's languages. |
| Approach: | They propose to use a universal tagset to annotate morphological data using a schema that includes a lemma and a bundle of morphology features. |
| Outcome: | The project releases annotated morphological data using a universal tagset, the UniMorph schema. |
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages (2023.findings-emnlp)
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Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, Vera Axelrod, Isaac Caswell, Colin Cherry, Dan Garrette, Reeve Ingle, Melvin Johnson, Dmitry Panteleev, Partha Talukdar
| Challenge: | Existing datasets are often informed by established research directions in the NLP community. |
| Approach: | They propose a benchmark to evaluate the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
| Outcome: | The proposed benchmark evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
UniMorph 3.0: Universal Morphology (2020.lrec-1)
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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
| Challenge: | Explicit modeling of morphology has demonstrable benefits for language modeling, speech recognition, word embedding and keyword search. |
| Approach: | They propose a language-independent feature schema for rich morphological annotation and a type-level resource for annotated data in diverse languages. |
| Outcome: | The proposed schema has been improved to make it more complete and correct, and adds 66 new languages and parts of speech for 12 languages. |