Brian Roark
Brian Roark is a computational linguist working on various topics in natural language processing. His research interests include: language modeling for automatic speech recognition, text entry and other applications; text normalization and transliteration; text entry, accessibility and augmentative and alternative communication (AAC).
Before joining Google, he was a faculty member for 9 years in the Center for Spoken Language Understanding (CSLU) at Oregon Health & Science University (OHSU) – part of what used to be the Oregon Graduate Institute (OGI). Before that, he was in the Speech Algorithms Department at AT&T Labs - Research from 2001–2004. He received his Ph.D. in the Department of Cognitive and Linguistic Sciences at Brown University in 2001.
More information, including publications, CV and other links, can be found at his external webpage here.
Before joining Google, he was a faculty member for 9 years in the Center for Spoken Language Understanding (CSLU) at Oregon Health & Science University (OHSU) – part of what used to be the Oregon Graduate Institute (OGI). Before that, he was in the Speech Algorithms Department at AT&T Labs - Research from 2001–2004. He received his Ph.D. in the Department of Cognitive and Linguistic Sciences at Brown University in 2001.
More information, including publications, CV and other links, can be found at his external webpage here.
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While the Latin script is used informally by speakers of many languages with more complex native scripts, high quality Latin script corpora for such languages that reflect actual natural romanizations are scarce and often difficult to collect. In this work, we propose a method for mining romanized language corpora in languages for which we do not have any pre-existing samples of naturally romanized text, focusing on Tigrinya as a test case. First we examine the efficacy of learning romanizations for a language based on observed romanizations in other languages that use the same native script. We then extrinsically assess such methods by using a romanization model trained on Amharic data to bootstrap coverage of romanized Tigrinya in a language identification system. Manual evaluation by two L1 and one L2 Tigrinya speakers suggests our method extracts romanized Tigrinya text with acceptably high precision. We release code to run our mining pipeline on public web corpora, such as MADLAD-400.
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The Private-Use Area (PUA) is an important part of the Unicode standard. It consists of several ranges of Unicode code points with no official character assignments. The PUA is primarily used as a temporary representation mechanism for characters outside the official standard to facilitate text entry and display of orthographies that cannot be adequately represented by other means. The primary downside of PUA is that characters lose their semantics if the pairing with the corresponding display font is broken. Consequently, they cannot be faithfully displayed in the general setting. Large-scale multilingual web corpora inevitably contain PUA code points of unclear provenance. We investigate the distribution of PUA characters within large-scale datasets, using filters for determining PUA tokens of linguistic interest. We analyze the resulting distributions both across scripts and writing systems, and show that PUA-bearing tokens can signal texts from under-represented languages. We explore whether an off-the-shelf large language model (LLM) can classify PUA characters as those that constitute relevant orthographic signals vs. punctuation or other noise. While the proportion of PUA-bearing paragraphs in the original corpora are small, we identify millions of paragraphs, and we argue that such data is still important for the long tail of data-scarce orthographies. Moreover, as a primary Unicode mechanism for poorly represented writing systems, the PUA is here to stay.
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The Abkhaz-Adyghe and Nakh-Daghestanian language families encompass 35 living languages that possess arguably the most complex modern Cyrillic orthographies due to their very sophisticated phonology. The relevant online data displays idiosyncratic patterns among which the use of confusable characters in input methods is the most prevalent. This work studies one such character---letter \emph{palochka}---that is shared by most writing systems in question. We investigate whether the patterns including variants of this character alone can act as language data markers when mining these languages in a large-scale web-crawled data. Using a wide-coverage off-the-shelf LID model (GlotLID) we further investigate the data mined using such patterns and estimate the effects of confusable character normalization on quality of paragraph-level LID predictions in 14 supported languages. According to GlotLID, the normalization significantly increases the recall (discovery of new language data) for some languages while degrading it for others. However, manual evaluation reveals that only 41\% of wins and 46\% of losses are accurate due to GlotLID prediction errors. We argue that despite finding useful signal higher precision LID approaches tailored to these long-tail languages are needed to improve the quality of mined data.
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Improving Informally Romanized Language Identification
Adrian Benton
Christo Kirov
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Suzhou, China, 2318–2336
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The Latin script is often used informally to write languages with non-Latin native scripts. In many cases (e.g., most languages in India), there is no orthography, meaning that there is no conventional spelling of words in the Latin script, hence there will be high spelling variability in written text. Such romanization can render languages that are normally easily distinguished based on script highly confusable, such as Hindi and Urdu. In this work, we present methods to improve language identification of romanized text by improving methods to synthesize training sets. We find that training on synthetic samples which incorporate natural spelling variation yields higher language identification system accuracy than including available naturally occurring examples in the training set or even training higher capacity models. We demonstrate new state-of-the-art language identification performance on romanized text from 20 Indic languages in the Bhasha-Abhijnaanam evaluation set (Madhani et al., 2023a), improving test F1 from the reported 74.7% (using a pretrained neural model) to 85.4% using a linear classifier trained solely on synthetic data and 88.2% when also training on available harvested text.
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Context-aware Transliteration of Romanized South Asian Languages
Christo Kirov
Computational Linguistics, 50 (2) (2024), 475–534
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While most transliteration research is focused on single tokens such as named entities -- e.g., transliteration of "અમદાવાદ" from the Gujarati script to the Latin script "Ahmedabad" -- the informal romanization prevalent in South Asia and elsewhere often requires transliteration of full sentences. The lack of large parallel text collections of full sentence (as opposed to single word) transliterations necessitates incorporation of contextual information into transliteration via non-parallel resources, such as via mono-script text collections. In this paper, we present a number of methods for improving transliteration in context for such a use scenario. Some of these methods in fact improve performance without making use of sentential context, allowing for better quantification of the degree to which contextual information in particular is responsible for system improvements. Our final systems, which ultimately rely upon ensembles including large pretrained language models finetuned on simulated parallel data, yield substantial improvements over the best previously reported results for full sentence transliteration from Latin to native script on all 12 languages in the Dakshina dataset (Roark et al. 2020), with an overall 4.8% absolute (27.1% relative) mean word-error rate reduction.
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XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
Sebastian Ruder
Mihir Sanjay Kale
Shruti Rijhwani
Jean-Michel Sarr
Cindy Wang
John Wieting
Christo Kirov
Dana L. Dickinson
Bidisha Samanta
Connie Tao
David Adelani
Vera Axelrod
Reeve Ingle
Dmitry Panteleev
Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, Singapore, pp. 1856-1884
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Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) — languages for which NLP research is particularly far behind in meeting user needs — it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks — tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP 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 that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text only, multi-modal (vision, audio, and text), supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models.
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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.
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Design principles of an open-source language modeling microservice package for AAC text-entry applications
9th Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022), Association for Computational Linguistics (ACL), Dublin, Ireland, pp. 1-16
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We present MozoLM, an open-source language model microservice package intended for use in AAC text-entry applications, with a particular focus on the design principles of the library. The intent of the library is to allow the ensembling of multiple diverse language models without requiring the clients (user interface designers, system users or speech-language pathologists) to attend to the formats of the models. Issues around privacy, security, dynamic versus static models, and methods of model combination are explored and specific design choices motivated. Some simulation experiments demonstrating the benefits of personalized language model ensembling via the library are presented.
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Beyond Arabic: Software for Perso-Arabic Script Manipulation
Raiomond Doctor
Richard Sproat
Proceedings of the 7th Arabic Natural Language Processing Workshop (WANLP2022) at EMNLP, Association for Computational Linguistics (ACL), Abu Dhabi, United Arab Emirates (Hybrid), pp. 381-387
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This paper presents an open-source software library that provides a set of finite-state transducer (FST) components and corresponding utilities for manipulating the writing systems of languages that use the Perso-Arabic script. The operations include various levels of script normalization, including visual invariance-preserving operations that subsume and go beyond the standard Unicode normalization forms, as well as transformations that modify the visual appearance of characters in accordance with the regional orthographies for ten contemporary languages from diverse language families. The library also provides simple FST-based romanization and transliteration. We additionally attempt to formalize the typology of Perso-Arabic characters by providing one-to-many mappings from Unicode code points to the languages that use them. While our work focuses on the Arabic script diaspora rather than Arabic itself, this approach could be adopted for any language that uses the Arabic script, thus providing a unified framework for treating a script family used by close to a billion people.
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Graphemic Normalization of the Perso-Arabic Script
Raiomond Doctor
Richard Sproat
Proceedings of Grapholinguistics in the 21st Century, 2022 (G21C, Grafematik), Fluxus Editions, Brest, France, pp. 315-376
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Since its original appearance in 1991, the Perso-Arabic script representation in Unicode has grown from 169 to over 440 atomic isolated characters spread over several code pages representing standard letters, various diacritics and punctuation for the original Arabic and numerous other regional orthographic traditions (Unicode Consortium, 2021). This paper documents the challenges that Perso-Arabic presents beyond the best-documented languages, such as Arabic and Persian, building on earlier work by the expert community (ICANN, 2011, 2015). We particularly focus on the situation in natural language processing (NLP), which is affected by multiple, often neglected, issues such as the use of visually ambiguous yet canonically nonequivalent letters and the mixing of letters from different orthographies. Among the contributing conflating factors are the lack of input methods, the instability of modern orthographies (e.g., Aazim et al., 2009; Iyengar, 2018), insufficient literacy, and loss or lack of orthographic tradition (Jahani and Korn, 2013; Liljegren, 2018). We evaluate the effects of script normalization on eight languages from diverse language families in the Perso-Arabic script diaspora on machine translation and statistical language modeling tasks. Our results indicate statistically significant improvements in performance in most conditions for all the languages considered when normalization is applied. We argue that better understanding and representation of Perso-Arabic script variation within regional orthographic traditions, where those are present, is crucial for further progress of modern computational NLP techniques (Ponti et al., 2019; Conneau et al., 2020; Muller et al., 2021) especially for languages with a paucity of resources.
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