Papers with XQA

2 papers
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks (D19-1)

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Challenge: Existing models that can handle cross-lingual tasks with limited or no training data are insensitive to different languages.
Approach: They propose to use Unicoder to train models in one language and apply it to other languages.
Outcome: Experiments show that Unicoder learns the mappings among different languages from more perspectives.
Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering (2023.acl-long)

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Challenge: Existing XQA methods focus on reasoning on a single knowledge source, e.g., structured knowledge bases, unstructured corpora, etc. Existing work in XQA focuses on integrating information from heterogeneous knowledge sources.
Approach: They propose to leverage question decomposing for heterogeneous knowledge integration by breaking down a complex question into simpler ones and selecting the appropriate knowledge source for each sub-question.
Outcome: The proposed framework outperforms SOTA methods on complex QA datasets.

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