Alexander Gutkin

Alexander Gutkin

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    Improving Informally Romanized Language Identification
    Adrian Benton
    Christo Kirov
    Proceedings of EMNLP (2025) (to appear)
    Preview abstract 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. View details
    Preview abstract 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. View details
    Helpful Neighbors: Leveraging Neighbors in Geographic Feature Pronunciation
    Lion Jones
    Richard William Sproat
    Haruko Ishikawa
    Transactions of the Association for Computational Linguistics, 11 (2023), 85–101
    Preview abstract If one sees the place name Houston Mercer Dog Run in New York, how does one know how to pronounce it? Assuming one knows that Houston in New York is pronounced ˈhaʊstən and not like the Texas city (ˈhjuːstən), then one can probably guess that ˈhaʊstən is also used in the name of the dog park. We present a novel architecture that learns to use the pronunciations of neighboring names in order to guess the pronunciation of a given target feature. Applied to Japanese place names, we demonstrate the utility of the model to finding and proposing corrections for errors in Google Maps. To demonstrate the utility of this approach to structurally similar problems, we also report on an application to a totally different task: Cognate reflex prediction in comparative historical linguistics. A version of the code has been open-sourced. View details
    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
    Preview abstract 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. View details
    Extensions to Brahmic script processing within the Nisaba library: new scripts, languages and utilities
    Raiomond Doctor
    Proceedings of the 13th Language Resources and Evaluation Conference.(LREC), European Language Resources Association (ELRA), 20-25 June, Marseille, France (2022), 6450‑6460
    Preview abstract The Brahmic family of scripts is used to record some of the most spoken languages in the world and is arguably the most diverse family of writing systems. In this work, we present several substantial extensions to Brahmic script functionality within the open-source Nisaba library of finite-state script normalization and processing utilities (Johny et. al. , 2021). First, we extend coverage from the original ten scripts to an additional ten scripts of South Asia and beyond, including some used to record endangered languages such as Dogri. Second, we augment the language layer so that scripts used by multiple languages in distinct ways can be processed correctly for more languages, such as the Bengali script when used for the low-resource language Santali. We document key changes to the finite-state engine required to support these new languages and scripts. Finally, we add new script processing utilities, including lightweight script-level reading normalization that (unlike existing visual normalization) does not preserve visual invariance, and a fixed-input transliteration mechanism specifically tailored to Brahmic text entry with ASCII characters. View details
    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
    Preview abstract 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. View details
    Mockingbird at the SIGTYP 2022 Shared Task: Two Types of Models for Prediction of Cognate Reflexes
    Christo Kirov
    Richard Sproat
    Proceedings of the 4th Workshop on Research in Computational Typology and Multilingual NLP (SIGTYP 2022) at NAACL, Association for Computational Linguistics (ACL), Seattle, WA, pp. 70-79
    Preview abstract 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. View details
    Criteria for Useful Automatic Romanization in South Asian Languages
    Proceedings of the 13th Language Resources and Evaluation Conference.(LREC), European Language Resources Association (ELRA), 20-25 June, Marseille, France (2022), 6662‑6673
    Preview abstract This paper presents a number of possible criteria for systems that transliterate South Asian languages from their native scripts into the Latin script. This process is also known as romanization. These criteria are related to either fidelity to human linguistic behavior (pronunciation transparency, naturalness and conventionality) or processing utility for people (ease of input) as well as under-the-hood in systems (invertibility and stability across languages and scripts). When addressing these differing criteria several linguistic considerations, such as modeling of prominent phonological processes and their relation to orthography, need to be taken into account. We discuss these key linguistic details in the context of Brahmic scripts and languages that use them, such as Hindi and Malayalam. We then present the core features of several romanization algorithms, implemented in finite state transducer (FST) formalism, that address differing criteria. Implementation of these algorithms will be released as part of the Nisaba finite-state script processing library. View details
    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
    Preview abstract 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. View details
    Building Machine Translation Systems for the Next Thousand Languages
    Ankur Bapna
    Julia Kreutzer
    Aditya Siddhant
    Mengmeng Niu
    Pallavi Nikhil Baljekar
    Xavier Garcia
    Vera Saldinger Axelrod
    Yuan Cao
    Maxim Krikun
    Pidong Wang
    Apu Shah
    Yanping Huang
    Zhifeng Chen
    Yonghui Wu
    Macduff Richard Hughes
    Google Research (2022)
    Preview abstract In this paper we share findings from our effort towards building practical machine translation (MT) systems capable of translating across over one thousand languages. We describe results across three research domains: (i) Building clean, web-mined datasets by leveraging semi-supervised pre-training for language-id and developing data-driven filtering techniques; (ii) Leveraging massively multilingual MT models trained with supervised parallel data for over $100$ languages and small monolingual datasets for over 1000 languages to enable translation for several previously under-studied languages; and (iii) Studying the limitations of evaluation metrics for long tail languages and conducting qualitative analysis of the outputs from our MT models. We hope that our work provides useful insights to practitioners working towards building MT systems for long tail languages, and highlights research directions that can complement the weaknesses of massively multilingual pre-trained models in data-sparse settings. View details