Papers by Meizhen Liu
A Copy-Augmented Generative Model for Open-Domain Question Answering (2022.acl-short)
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| Challenge: | Existing open-domain question answering approaches follow a two-stage paradigm retriever then reader. |
| Approach: | They propose a novel reader-based generative approach that incorporates extractive and generative readers. |
| Outcome: | The proposed model improves on two benchmark datasets, Natural Questions and TriviaQA. |
Adapters for Enhanced Modeling of Multilingual Knowledge and Text (2022.findings-emnlp)
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| Challenge: | Large language models learn facts from text corpora, but knowledge graphs contain facts in an explicit triple format, restricting their research and application. |
| Approach: | They propose to enhance multilingual language models with knowledge from multilingual knowledge graphs . they propose to use cross-lingual entity alignment and facts from MLKGs to improve performance . |
| Outcome: | The proposed model improves MLLMs with cross-lingual entity alignment and facts from multilingual knowledge graphs for many languages while maintaining performance on other general language tasks. |
InteMATs: Integrating Granularity-Specific Multilingual Adapters for Cross-Lingual Transfer (2023.findings-emnlp)
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| Challenge: | Existing work relies on full-model fine-tuning on large parallel datasets to enhance cross-lingual alignment of MLLMs. |
| Approach: | They propose an approach that integrates multilingual adapters trained on texts of different levels of granularity into multilingual models. |
| Outcome: | The proposed approach improves the performance of multilingual language models on low-resource languages. |