Papers with annotation projection
Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study (D19-61)
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| Challenge: | Cross-lingual dependency parsing involves transferring syntactic knowledge from one language to another. |
| Approach: | They compare two approaches to cross-lingual dependency parsing using monolingual source models and a polyglot model which is trained on the combination of all source languages. |
| Outcome: | The proposed methods improve low-resource dependency parsers by transferring syntactic knowledge from one language to another. |
PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale (2023.findings-emnlp)
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| Challenge: | Existing question answering systems rely on large, high-quality training data. |
| Approach: | They propose a synthetic data generation method which decomposes cross-lingual QA into two stages . they apply a question generation model to the English side and annotation projection to translate both questions and answers. |
| Outcome: | The proposed method outperforms existing methods on cross-lingual QA datasets. |
Multilingual Coreference Resolution in Multiparty Dialogue (2023.tacl-1)
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| Challenge: | Existing datasets for entity coreference resolution are limited to English and other languages are rare. |
| Approach: | They propose to use TV transcripts to create multilingual multiparty coreference datasets that leverage existing subtitles in Chinese and Farsi. |
| Outcome: | The proposed dataset re-annotates for coreference on TV transcripts and then leverages existing subtitle translations to create a multilingual corpus. |
Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging (D18-1)
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| Challenge: | Low-resource languages lack manual annotated data to learn basic models such as part-of-speech (POS) taggers. |
| Approach: | They propose a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision in a uniform framework. |
| Outcome: | The proposed model scales to hundreds of low-resource languages without access to gold annotated data. |
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need! (C18-1)
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| Challenge: | Argumentation mining (AM) requires the identification of complex discourse structures . existing resources are not adequate for assessing cross-lingual AM due to their heterogeneity or lack of complexity. |
| Approach: | They propose to use a dataset to translate persuasive student essays into German, French, Spanish, and Chinese to compare arguments mining and annotation projection. |
| Outcome: | The proposed methods perform better when using expensive human or cheap machine translations and almost eliminate loss from cross-lingual transfer. |
Cross-lingual Annotation Projection in Legal Texts (2020.coling-main)
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| Challenge: | a new study examines annotation projection in text classification problems where source documents are published in multiple languages. |
| Approach: | They propose to use word embeddings and dynamic time warping to create an annotation corpus for text classification problems where source documents are published in multiple languages. |
| Outcome: | The proposed method is based on word embeddings and dynamic time warping . the aim is to train linguistic tools for the target language without experts . |
Frame Shift Prediction (2022.lrec-1)
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| Challenge: | Frame shift is a cross-linguistic phenomenon in translation which results in corresponding pairs of linguistic material evoking different frames. |
| Approach: | They propose a task to predict cross-linguistic frame-to-frame correspondence and propose auxiliary training to learn cross-lingual frame-by-frame correlation. |
| Outcome: | The proposed task can learn cross-linguistic frame-to-frame correspondence and predict frame shifts in a Berkeley FrameNet-like configuration. |
Shallow Discourse Parsing for Under-Resourced Languages: Combining Machine Translation and Annotation Projection (2020.lrec-1)
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| Challenge: | Shallow Discourse Parsing (SDP) relies on large amounts of training data, which so far exists only for English. |
| Approach: | They propose to translate an existing English Penn Discourse TreeBank into German and use it to create a German corpus annotated for shallow discourse relations in the news domain. |
| Outcome: | The proposed corpus is annotated for shallow discourse relations in the (financial) news domain. |
ZAP: An Open-Source Multilingual Annotation Projection Framework (L18-1)
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| Challenge: | Existing frameworks for annotation projection in parallel corpora limit reproducibility and comparison of experiments. |
| Approach: | They propose an open-source framework for annotation projection in parallel corpora . framework is Java-based and includes methods for preprocessing corpors, computations and visualization . |
| Outcome: | The proposed framework is designed for ease-of-use with lightweight APIs. |
Frustratingly Easy Label Projection for Cross-lingual Transfer (2023.findings-acl)
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| Challenge: | Existing approaches to improve cross-lingual transfer performance are based on word alignment, but no empirical studies have evaluated their effectiveness or limitations. |
| Approach: | They propose a mark-then-translate method that integrates translation and projection by inserting special markers around the labeled spans in the original sentence. |
| Outcome: | The proposed method outperforms word alignment-based methods in 57 languages and three tasks. |
Multitask Learning for Cross-Lingual Transfer of Broad-coverage Semantic Dependencies (2020.emnlp-main)
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| Challenge: | Existing methods for developing broad-coverage semantic dependency parsers for languages without semantically annotated data are limited to English, Czech and Chinese. |
| Approach: | They propose a multitask learning framework coupled with annotation projection to build broad-coverage semantic dependency parsers for languages without annotated resources. |
| Outcome: | The proposed model improves labeled F1 score on multitask tasks from English to Czech compared to baseline models . |
Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles (2020.lrec-1)
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| Challenge: | Existing semantic role labeling resources for Hebrew are not available in English. |
| Approach: | They propose a semantic role labeling resource for Hebrew built semi-automatically through annotation projection from English to Hebrew. |
| Outcome: | The proposed resource is built semi-automatically from an English dataset . it includes morphological analysis, dependency syntax and semantic role labeling . |
T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks (2023.findings-emnlp)
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| Challenge: | Annotation projection is a task of transporting labels from source to target language without high-quality training data. |
| Approach: | They propose an annotation projection approach that leverages pretrained text2text models and machine translation technology to generate annotated data. |
| Outcome: | The proposed approach outperforms existing methods on intrinsic and extrinsic tasks in 5 Indo-European and 8 low-resource African languages. |