Challenge: Knowledge bases (KBs) in low-resource languages are often incomplete, restricting the ability to do zero-shot question answering using multilingual language models.
Approach: They propose a novel cross-lingual mapping technique which improves word alignments extracted from parallel English-LRL text by combining lexical alignment, named entity recognition, and semantic alignment.
Outcome: The proposed approach improves zero-shot question answering accuracy by up to 17% compared to baselines without KB access.

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Challenge: Existing approaches to solve multilingual question answering over knowledge graph (KGQA) use of bilingual lexicon induction to map training questions into those in target language circumvents language inconsistency .
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Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective (2025.findings-emnlp)

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Challenge: Existing approaches to cross-lingual Named Entity Recognition focus on Latin script language (LSL) for non-Latin script language, performance often degrades due to deep structural differences.
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A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (2022.coling-1)

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Challenge: Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries . however, its effect is limited by the gap between embedding clusters of different languages .
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CrossAligner & Co: Zero-Shot Transfer Methods for Task-Oriented Cross-lingual Natural Language Understanding (2022.findings-acl)

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Challenge: Task-oriented personal assistants enable people to interact with devices and services using natural language.
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Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking (2021.findings-acl)

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Challenge: Existing work on cross-language entity linking grounds mentions written in multiple languages to a monolingual knowledge base is lacking.
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Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)

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Challenge: Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages .
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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.
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Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing (2023.tacl-1)

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Challenge: Existing work on cross-lingual semantic parsing has focused on English . a few-shot approach to parse from natural languages is comparatively unexplored .
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Enhancing Cross-lingual Sentence Embedding for Low-resource Languages with Word Alignment (2024.findings-naacl)

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Challenge: Current approaches to obtain cross-lingual sentence embeddings rely on pre-trained language models that implicitly align the contextual representations of similar units of sentences in different languages.
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Zero-Shot Cross-lingual Semantic Parsing (2022.acl-long)

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Challenge: Recent work in cross-lingual semantic parsing assumes access to high-quality machine translation systems and word alignment tools.
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