Challenge: Existing methods to translate sentences to other languages using heuristics are challenging.
Approach: They propose a model that learns hierarchical weights for different sets of labels and applies them to other languages to translate them.
Outcome: The proposed model can translate English datasets to other languages and obtain different sets of labels again using heuristics.

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Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)

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Challenge: Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved.
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Multilingual Generation in Abstractive Summarization: A Comparative Study (2024.lrec-main)

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Challenge: Existing models for multilingual generation lack thorough analysis due to extensive linguistic diversity.
Approach: They propose to classify multilingual generation methodologies into three categories based on their underlying modeling principles . they introduce an automatic metric to mitigate spurious correlations associated with language mixing .
Outcome: The proposed model improves in high-resource, low-resourced, and zero-shot scenarios.
Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations (D19-61)

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Challenge: Pretrained sentence representations have set the new state of the art in many language understanding tasks.
Approach: They propose to use a multilingual corpus to train deep bidirectional sentence representations that are fully lexicalized to allow for the development of an unsupervised universal dependency parser.
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Pre-trained Language Models Can be Fully Zero-Shot Learners (2023.acl-long)

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Challenge: Existing approaches to pre-trained language models require fine-tuning on labeled datasets or manually constructing proper prompts.
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Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing (2021.emnlp-main)

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Challenge: Existing training data is limited for languages other than English, so is the performance of the developed parsers.
Approach: They propose to apply a pre-trained multilingual model to Italian, German and Dutch parsers where only a small number of manually annotated parses are available.
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Monolingual Adapters for Zero-Shot Neural Machine Translation (2020.emnlp-main)

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Challenge: Existing adapter layers are more parameter-efficient and provide better performance than bilingual ones.
Approach: They propose to use monolingual adapter layers instead of bilingual ones to compose them and generalize to unseen language pairs.
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MLSUM: The Multilingual Summarization Corpus (2020.emnlp-main)

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Challenge: Existing biases in multi-lingual datasets are limiting the use of multilingual data in document summarization tasks.
Approach: They present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
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Towards Unifying Multi-Lingual and Cross-Lingual Summarization (2023.acl-long)

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Challenge: Existing work on multilingual summarization and cross-lingual summmarization has been limited due to their different definitions.
Approach: They propose to unify MLS and CLS into a more general setting, i.e. many-to-many summarization.
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Evaluating the Factuality of Zero-shot Summarizers Across Varied Domains (2024.eacl-short)

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Challenge: Recent work has shown that large language models can generate zero-shot summaries without explicit supervision that are often comparable or even preferred to manually composed reference summary.
Approach: They evaluate large language models (LLMs) that generate zero-shot summaries without explicit supervision that are often comparable to manual reference summary . they acquire annotations from domain experts to identify inconsistencies in summaires and categorize errors.
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Language-Independent Representations Improve Zero-Shot Summarization (2024.naacl-short)

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Challenge: Pretrained models can be fine tuned on downstream generation tasks, but they can fail in zero-shot conditions.
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