Petra Galuščáková

Also published as: Petra Galuscakova


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Cross-language Sentence Selection via Data Augmentation and Rationale Training
Yanda Chen | Chris Kedzie | Suraj Nair | Petra Galuscakova | Rui Zhang | Douglas Oard | Kathleen McKeown
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This paper proposes an approach to cross-language sentence selection in a low-resource setting. It uses data augmentation and negative sampling techniques on noisy parallel sentence data to directly learn a cross-lingual embedding-based query relevance model. Results show that this approach performs as well as or better than multiple state-of-the-art machine translation + monolingual retrieval systems trained on the same parallel data. Moreover, when a rationale training secondary objective is applied to encourage the model to match word alignment hints from a phrase-based statistical machine translation model, consistent improvements are seen across three language pairs (English-Somali, English-Swahili and English-Tagalog) over a variety of state-of-the-art baselines.

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Segmenting Subtitles for Correcting ASR Segmentation Errors
David Wan | Chris Kedzie | Faisal Ladhak | Elsbeth Turcan | Petra Galuscakova | Elena Zotkina | Zhengping Jiang | Peter Bell | Kathleen McKeown
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Typical ASR systems segment the input audio into utterances using purely acoustic information, which may not resemble the sentence-like units that are expected by conventional machine translation (MT) systems for Spoken Language Translation. In this work, we propose a model for correcting the acoustic segmentation of ASR models for low-resource languages to improve performance on downstream tasks. We propose the use of subtitles as a proxy dataset for correcting ASR acoustic segmentation, creating synthetic acoustic utterances by modeling common error modes. We train a neural tagging model for correcting ASR acoustic segmentation and show that it improves downstream performance on MT and audio-document cross-language information retrieval (CLIR).


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MATERIALizing Cross-Language Information Retrieval: A Snapshot
Petra Galuscakova | Douglas Oard | Joe Barrow | Suraj Nair | Shing Han-Chin | Elena Zotkina | Ramy Eskander | Rui Zhang
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)

At about the midpoint of the IARPA MATERIAL program in October 2019, an evaluation was conducted on systems’ abilities to find Lithuanian documents based on English queries. Subsequently, both the Lithuanian test collection and results from all three teams were made available for detailed analysis. This paper capitalizes on that opportunity to begin to look at what’s working well at this stage of the program, and to identify some promising directions for future work.


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Low Resource Methods for Medieval Document Sections Analysis
Petra Galuščáková | Lucie Neužilová
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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PhraseFix: Statistical Post-Editing of TectoMT
Petra Galuščáková | Martin Popel | Ondřej Bojar
Proceedings of the Eighth Workshop on Statistical Machine Translation


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Selecting Data for English-to-Czech Machine Translation
Aleš Tamchyna | Petra Galuščáková | Amir Kamran | Miloš Stanojević | Ondřej Bojar
Proceedings of the Seventh Workshop on Statistical Machine Translation

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The Joy of Parallelism with CzEng 1.0
Ondřej Bojar | Zdeněk Žabokrtský | Ondřej Dušek | Petra Galuščáková | Martin Majliš | David Mareček | Jiří Maršík | Michal Novák | Martin Popel | Aleš Tamchyna
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

CzEng 1.0 is an updated release of our Czech-English parallel corpus, freely available for non-commercial research or educational purposes. In this release, we approximately doubled the corpus size, reaching 15 million sentence pairs (about 200 million tokens per language). More importantly, we carefully filtered the data to reduce the amount of non-matching sentence pairs. CzEng 1.0 is automatically aligned at the level of sentences as well as words. We provide not only the plain text representation, but also automatic morphological tags, surface syntactic as well as deep syntactic dependency parse trees and automatic co-reference links in both English and Czech. This paper describes key properties of the released resource including the distribution of text domains, the corpus data formats, and a toolkit to handle the provided rich annotation. We also summarize the procedure of the rich annotation (incl. co-reference resolution) and of the automatic filtering. Finally, we provide some suggestions on exploiting such an automatically annotated sentence-parallel corpus.


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Two-step translation with grammatical post-processing
David Mareček | Rudolf Rosa | Petra Galuščáková | Ondřej Bojar
Proceedings of the Sixth Workshop on Statistical Machine Translation