Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings

García-Ferrero, Iker; Agerri, Rodrigo; Rigau, Germán

doi:10.18653/v1/2022.findings-emnlp.478

Cited by 1 publication

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use the English SemEval 2016 Aspect Based Sentiment Analysis (ABSA) datasets (Pontiki et al, 2014). Additionally, for the evaluation we also used the parallel versions for Spanish, French and Russian generated via machine translation and manual projection of the labels (García-Ferrero et al, 2022).…”

Section: Datasetsmentioning

confidence: 99%

“…We tested different models as backbone with no improvement in performance (see Appendix C). We use these four systems to compute word alignments between the source and the target sentences and generate the label projections applying the algorithm published by García-Ferrero et al (2022) 3 .…”

Section: Baselinesmentioning

confidence: 99%

“…We augment the parallel data with 50,000 random parallel sentences from ParaCrawl v8 (Esplà et al, 2019). Models are evaluated with respect to the manually label projections generated by García-Ferrero et al (2022). As the Europarlbased NER dataset (Agerri et al, 2018) provides only test data for each language, T-Projection and XLM-RoBERTa are trained using the full English CoNLL 2003 dataset (Tjong Kim Sang, 2002) together with the labeled English Europarl test data.…”

Section: Intrinsic Evaluationmentioning

confidence: 99%

“…The majority of previous published work on this line of research explores the application of word-alignments (Ehrmann et al, 2011). However, projection methods based on word-alignments have achieved mixed results as they often produce partial, incorrect or missing annotation projections (García-Ferrero et al, 2022). This is due to the fact that word alignments are computed on a word-byword basis leveraging word co-occurrences or similarity between word vector representations.…”

Section: Introductionmentioning

confidence: 99%

“…Fei et al (2020 use the word alignment probabilities calculated with FastAlign (Dyer et al, 2013) and the POS tag distributions of the source and target words to project from the source corpus into a target language machine-translated corpus. Finally, García-Ferrero et al (2022) propose an annotation projection method based on machine translation and AWESOME (Dou and Neubig, 2021), Transformerbased word alignments to automatically project datasets from English to other languages.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks

García-Ferrero,

Agerri,

Rigau

2023

Findings of the Association for Computational Linguistics: EMNLP 2023

View full text Add to dashboard Cite

In the absence of readily available labeled data for a given sequence labeling task and language, annotation projection has been proposed as one of the possible strategies to automatically generate annotated data. Annotation projection has often been formulated as the task of transporting, on parallel corpora, the labels pertaining to a given span in the source language into its corresponding span in the target language. In this paper we present T-Projection, a novel approach for annotation projection that leverages large pretrained text-to-text language models and state-of-the-art machine translation technology. T-Projection decomposes the label projection task into two subtasks: (i) A candidate generation step, in which a set of projection candidates using a multilingual T5 model is generated and, (ii) a candidate selection step, in which the generated candidates are ranked based on translation probabilities. We conducted experiments on intrinsic and extrinsic tasks in 5 Indo-European and 8 lowresource African languages. We demostrate that T-projection outperforms previous annotation projection methods by a wide margin. We believe that T-Projection can help to automatically alleviate the lack of high-quality training data for sequence labeling tasks. Code and data are publicly available. 1

show abstract

Section: Datasetsmentioning

confidence: 99%

Section: Baselinesmentioning

confidence: 99%

Section: Intrinsic Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations