ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus

ImaniGooghari, Ayyoob; Sabet, Masoud Jalili; Dufter, Philipp; Cysou, Michael; Schütze, Hinrich

doi:10.18653/v1/2021.acl-demo.8

Cited by 4 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Visualizing word-level alignments was the aim of many tools such as Ugarit (Yousef et al, 2022b) and WA-Continuum (Steele and Specia, 2015), which visualizes word alignment of automatically aligned sentences to facilitate their evaluation. ImaniGooghari et al (2021) introduced the Parallel Corpus Explorer which supports exploring a word-aligned parallel corpus.…”

Section: Related Workmentioning

confidence: 99%

EVALIGN: Visual Evaluation of Translation Alignment Models

Yousef,

Heyer,

Jänicke

2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrati

View full text Add to dashboard Cite

This paper presents EVALIGN, a visual analytics framework for quantitative and qualitative evaluation of automatic translation alignment models. EVALIGN offers various visualization views enabling developers to visualize their models' predictions and compare the performance of their models with other baseline and state-of-the-art models. Through different search and filter functions, researchers and practitioners can also inspect the frequent alignment errors and their positions. EVALIGN hosts nine gold standard datasets and the predictions of multiple alignment models. The tool is extendable, and adding additional datasets and models is straightforward. EVALIGN can be deployed and used locally and is available on GitHub 1 .

show abstract

Section: Related Workmentioning

confidence: 99%

EVALIGN: Visual Evaluation of Translation Alignment Models

Yousef,

Heyer,

Jänicke

2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrati

View full text Add to dashboard Cite

show abstract

“…Multiparallel corpora are also used for language comparison (Mayer and Cysouw, 2012), typological studies (Östling, 2015;Asgari and Schütze, 2017) and PBMT (Nakov and Ng, 2012;Bertoldi et al, 2008;Dyer et al, 2013). ImaniGooghari et al (2021) provide a tool to browse a word-aligned multiparallel corpus, which can be used for the comparative study of languages and for error analysis in machine translation.…”

Section: Effect Of Word Frequencymentioning

confidence: 99%

Graph Neural Networks for Multiparallel Word Alignment

Imani,

Şenel,

Sabet

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

After a period of decrease, interest in word alignments is increasing again for their usefulness in domains such as typological research, cross-lingual annotation projection and machine translation. Generally, alignment algorithms only use bitext and do not make use of the fact that many parallel corpora are multiparallel. Here, we compute high-quality word alignments between multiple language pairs by considering all language pairs together. First, we create a multiparallel word alignment graph, joining all bilingual word alignment pairs in one graph. Next, we use graph neural networks (GNNs) to exploit the graph structure. Our GNN approach (i) utilizes information about the meaning, position and language of the input words, (ii) incorporates information from multiple parallel sentences, (iii) adds and removes edges from the initial alignments, and (iv) yields a prediction model that can generalize beyond the training sentences. We show that community detection provides valuable information for multiparallel word alignment. Our method outperforms previous work on three word alignment datasets and on a downstream task.

show abstract

“…Although the amount of text found in existing multiparallel corpora is far less than in monolingual corpora, we believe that they can serve as cross-lingual bridges, with which effective representation for low-resource languages can be derived. Highly multiparallel corpora have been used for expanding pretrained models to more languages (Ebrahimi and Kann, 2021;Wang et al, 2022), word alignment improvement and visualization (ImaniGooghari et al, 2021;, embedding learning (Dufter et al, 2018), and annotation projection (Agić et al, 2015b;Severini et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging

Imani¹,

Severini²,

Sabet³

et al. 2022

Preprint

View full text Add to dashboard Cite

Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many lowresource languages lack labeled data for training. An established method for training a POS tagger in such a scenario is to create a labeled training set by transferring from high-resource languages. In this paper, we propose a novel method for transferring labels from multiple high-resource source to low-resource target languages. We formalize POS tag projection as graph-based label propagation. Given translations of a sentence in multiple languages, we create a graph with words as nodes and alignment links as edges by aligning words for all language pairs. We then propagate node labels from source to target using a Graph Neural Network augmented with transformer layers. We show that our propagation creates training sets that allow us to train POS taggers for a diverse set of languages. When combined with enhanced contextualized embeddings, our method achieves a new state-ofthe-art for unsupervised POS tagging of low resource languages.

show abstract

ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus

Cited by 4 publications

References 22 publications

EVALIGN: Visual Evaluation of Translation Alignment Models

EVALIGN: Visual Evaluation of Translation Alignment Models

Graph Neural Networks for Multiparallel Word Alignment

Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging

Contact Info

Product

Resources

About