Papers by Elizabeth Salesky
Text Rendering Strategies for Pixel Language Models (2023.emnlp-main)
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| Challenge: | Recent approaches to rendering text use a large set of almost-equivalent input patches, which may prove sub-optimal for downstream tasks due to redundancy in the input representations. |
| Approach: | They propose four approaches to rendering text in a PIXEL model using character bigrams and patch frequency biases. |
| Outcome: | The proposed models perform better on sentence-level tasks without compromising performance on token-level or multilingual tasks. |
Tutorial: End-to-End Speech Translation (2021.eacl-tutorials)
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| Challenge: | Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation. |
| Approach: | This tutorial introduces the techniques used in cutting-edge research on speech translation. |
| Outcome: | The proposed models achieve state-of-the-art performance with end-to-end speech translation for both high- and low-resource languages. |
Fluent Translations from Disfluent Speech in End-to-End Speech Translation (N19-1)
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| Challenge: | Disfluency removal is an intermediate step between speech recognition and machine translation (MT) with the rise of end-to-end speech translation systems, disfluency recognition and removal needs to be incorporated into the model architectures or handled as a post-processing step. |
| Approach: | They propose to use a sequence-to-sequence model to translate from noisy, disfluent speech to fluent text with disfluencies removed using the recently collected ‘copy-edited’ references for the Fisher Spanish-English dataset. |
| Outcome: | The proposed model generates fluent translations from disfluent speech using the recently collected ‘copy-edited’ references for the Fisher Spanish-English dataset. |
Evaluating the IWSLT2023 Speech Translation Tasks: Human Annotations, Automatic Metrics, and Segmentation (2024.lrec-main)
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Matthias Sperber, Ondřej Bojar, Barry Haddow, Dávid Javorský, Xutai Ma, Matteo Negri, Jan Niehues, Peter Polák, Elizabeth Salesky, Katsuhito Sudoh, Marco Turchi
| Challenge: | a meta-analysis of human evaluation for speech translation has not been conducted . noisy data and segmentation mismatches are challenges for automatic metrics . |
| Approach: | They propose an evaluation strategy based on automatic resegmentation and direct assessment with segment context. |
| Outcome: | The proposed evaluation strategy is robust and scores well-correlated with other types of human judgements. |
Phone Features Improve Speech Translation (2020.acl-main)
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| Challenge: | End-to-end models for speech translation more tightly couple speech recognition (ASR) and machine translation (MT) compared to cascades, but performance gap remains in low-resource conditions . |
| Approach: | They propose two methods to incorporate phone features into current neural speech translation models. |
| Outcome: | The proposed models outperform existing models and cascades by up to 9 BLEU on low-resource conditions. |
Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation (P19-1)
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| Challenge: | Previous work on end-to-end translation from speech uses frame-level features as speech representations, which creates longer, sparser sequences than text. |
| Approach: | They propose a method to generate compressed phoneme-like speech representations that generate shorter, higher-level source sequences for translation. |
| Outcome: | The proposed method improves translation performance by 5 BLEU on high and low resource languages and reduces training time by 60%. |
A Corpus for Large-Scale Phonetic Typology (2020.acl-main)
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Elizabeth Salesky, Eleanor Chodroff, Tiago Pimentel, Matthew Wiesner, Ryan Cotterell, Alan W Black, Jason Eisner
| Challenge: | Existing multilingual speech corpora have limited data in many languages . existing corpus is limited to a small number of languages with available data . |
| Approach: | They propose a large-scale phonetic typology corpus with phoneme-level labels and phoneme alignments in 690 readings spanning 635 languages. |
| Outcome: | The proposed corpus covers 635 languages and includes acoustic-phonetic measures of vowels and sibilants. |
Multilingual Pixel Representations for Translation and Effective Cross-lingual Transfer (2023.emnlp-main)
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| Challenge: | Recent work shows that pixel representations can be finetuned across scripts without vocabulary extensions, adapters, or transliteration. |
| Approach: | They propose to use pixel representations to train multilingual machine translation models . they explore parameter sharing within and across scripts to better understand where they lead to positive transfer . |
| Outcome: | The proposed model improves on two multilingual datasets with different language coverage compared to subword embeddings . the proposed model can be finetuned cross-lingually or to unseen scripts, and is more data-efficient than other alternatives such as vocabulary expansion . |
A surprisal–duration trade-off across and within the world’s languages (2021.emnlp-main)
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| Challenge: | Throughout human evolution, countless languages have evolved, each with unique features. |
| Approach: | They analysed a corpus of 600 languages to find strong evidence for a surprisal–duration trade-off between languages and languages. |
| Outcome: | The proposed model shows that phones are produced faster in languages where they are less surprising and vice versa. |
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning (C18-2)
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Florian Dessloch, Thanh-Le Ha, Markus Müller, Jan Niehues, Thai-Son Nguyen, Ngoc-Quan Pham, Elizabeth Salesky, Matthias Sperber, Sebastian Stüker, Thomas Zenkel, Alexander Waibel
| Challenge: | In today's globalized world, communication is difficult and often the language barrier still prevents communication. |
| Approach: | They have developed a low-latency translation system that is adapted to lectures and covers several language pairs. |
| Outcome: | The proposed system improves performance but also covers several European languages. |
UniMorph 4.0: Universal Morphology (2022.lrec-1)
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Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Abbott Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud’hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova
| Challenge: | The Universal Morphology project provides broad-coverage instantiated morphological inflection tables for hundreds of diverse languages. |
| Approach: | They propose a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. |
| Outcome: | The proposed schema has added 66 new languages, including 24 endangered languages. |
WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects (2025.findings-acl)
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Daniel Deutsch, Eleftheria Briakou, Isaac Rayburn Caswell, Mara Finkelstein, Rebecca Galor, Juraj Juraska, Geza Kovacs, Alison Lui, Ricardo Rei, Jason Riesa, Shruti Rijhwani, Parker Riley, Elizabeth Salesky, Firas Trabelsi, Stephanie Winkler, Biao Zhang, Markus Freitag
| Challenge: | In order to evaluate large language models (LLMs), it is important to collect benchmark datasets in order to assess their multilingual performance. |
| Approach: | They extend the WMT24 dataset to cover 55 languages by collecting new human-written references and post-edits for 46 new languages/dialects. |
| Outcome: | The proposed dataset covers 55 languages and provides best-performing MT systems in all 55 languages. |
Generalized Entropy Regularization or: There’s Nothing Special about Label Smoothing (2020.acl-main)
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| Challenge: | Prior work has explored regularizing the output distributions of probabilistic models to alleviate overfitting. |
| Approach: | They propose a family of entropy regularizers that have a connection to regularization . they find that label smoothing provably does not allow for sparsity in an output distribution . |
| Outcome: | The proposed method improves the relationship between model entropy and performance on language generation tasks. |
Robust Open-Vocabulary Translation from Visual Text Representations (2021.emnlp-main)
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| Challenge: | MT models have discrete vocabularies and often use subword segmentation to achieve an ‘open vocabulary’. |
| Approach: | They propose to use visual text representations to create continuous vocabularies by processing visually rendered text with sliding windows. |
| Outcome: | The proposed models achieve 25.9 BLEU on character permuted German–English task, compared with traditional models on smaller and larger datasets. |