Papers with word-alignment
EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching (2022.findings-emnlp)
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| Challenge: | Existing methods for CS use dictionaries or parallel sentences with word-alignment to generate CS data by randomly switching words in a sentence. |
| Approach: | They propose a method that focuses on Entity-level Code-Switching to capture fine-grained cross-lingual semantics without corrupting syntax. |
| Outcome: | The proposed method captures fine-grained cross-lingual semantics without corrupting syntax. |
NegPar: A parallel corpus annotated for negation (L18-1)
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| Challenge: | NegPar is the first parallel corpus annotated for negation in the narrative domain. |
| Approach: | They present NegPar, a parallel corpus annotated for negation in the narrative domain . they follow the annotation guidelines in the CONANDOYLE-NEG corpus . |
| Outcome: | The proposed corpus is based on the CONANDOYLE-NEG corpus and is reannotated to ensure more consistent and interpretable representations. |
Multimodal Lexical Translation (L18-1)
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| Challenge: | Multimodal Lexical Translation (MLT) is a task that aims to translate ambiguous words given their context -an image and a sentence in the source language. |
| Approach: | They introduce a task to translate an ambiguous word given its context -an image and a sentence in the source language. |
| Outcome: | The proposed task is based on the Multi30K dataset and uses word-alignment followed by human inspection to select subsets of the dataset which are difficult to translate. |
Multilingual Coreference Resolution in Low-resource South Asian Languages (2024.lrec-main)
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| Challenge: | Existing coreference resolution models for South Asian languages are limited . a a sanity check for the prediction of translations is required to ensure accuracy of the model, authors say . |
| Approach: | They evaluate an end-to-end coreference resolution model on a Hindi golden set . they use translation and word-alignment tools to translate a translated dataset into 31 languages . |
| Outcome: | The proposed model scored 64 and 68 on a Hindi golden set. |