Anna Katanova


2023

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XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
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
Findings of the Association for Computational Linguistics: EMNLP 2023

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.

2020

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Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems
Fei He | Shan-Hui Cathy Chu | Oddur Kjartansson | Clara Rivera | Anna Katanova | Alexander Gutkin | Isin Demirsahin | Cibu Johny | Martin Jansche | Supheakmungkol Sarin | Knot Pipatsrisawat
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu, which are six of the twenty two official languages of India spoken by 374 million native speakers. The datasets are primarily intended for use in text-to-speech (TTS) applications, such as constructing multilingual voices or being used for speaker or language adaptation. Most of the corpora (apart from Marathi, which is a female-only database) consist of at least 2,000 recorded lines from female and male native speakers of the language. We present the methodological details behind corpora acquisition, which can be scaled to acquiring data for other languages of interest. We describe the experiments in building a multilingual text-to-speech model that is constructed by combining our corpora. Our results indicate that using these corpora results in good quality voices, with Mean Opinion Scores (MOS) > 3.6, for all the languages tested. We believe that these resources, released with an open-source license, and the described methodology will help in the progress of speech applications for the languages described and aid corpora development for other, smaller, languages of India and beyond.