Papers by Zhi Qu
Disentangling Pretrained Representation to Leverage Low-Resource Languages in Multilingual Machine Translation (2024.lrec-main)
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| Challenge: | Multilingual neural machine translation requires an enormous dataset, leaving the low-resource language (LRL) underdeveloped. |
| Approach: | They evaluated five languages using a parallel corpus of 1,000 instances each and found a zero-shot improvement of 7.4 from the baseline score of 7.1 to a score of 15.5 at best. |
| Outcome: | The proposed model improves performance in the linguistically diverse country of Indonesia by 7.4 from baseline score of 7.1 to 15.5 at best. |
Adapting to Non-Centered Languages for Zero-shot Multilingual Translation (2022.coling-1)
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| Challenge: | Existing studies attributed zero-shot translation to domination of central language, e.g. English, but we supplement this viewpoint with the strict dependence of non-centered languages. |
| Approach: | They propose a language-specific modeling method that adapts to non-centered languages to counteract the instability of zero-shot translation. |
| Outcome: | The proposed method performs better than baselines in centered data conditions and can easily fit non-centered data. |
XQ-MEval: A Dataset with Cross-lingual Parallel Quality for Benchmarking Translation Metrics (2026.findings-acl)
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| Challenge: | averaging metric scores across languages is suspicious since translations of equal quality receive different scores across language. |
| Approach: | They propose a semi-automatically built dataset to benchmark translation metrics using MQM-defined errors and a normalization strategy to mitigate cross-lingual scoring bias. |
| Outcome: | The proposed model shows that translation metrics suffer from cross-lingual scoring bias . the proposed model is based on a semi-automatically built dataset covering nine translation directions . |
Registering Source Tokens to Target Language Spaces in Multilingual Neural Machine Translation (2025.acl-long)
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| Challenge: | Multilingual neural machine translation (MNMT) aims for arbitrary translations across multiple languages. |
| Approach: | They propose a method that inserts a set of tokens specifying the target language into the input sequence between the source and target tokens. |
| Outcome: | The proposed method outperforms existing models on a large-scale benchmark. |
Exploring Intrinsic Language-specific Subspaces in Fine-tuning Multilingual Neural Machine Translation (2024.emnlp-main)
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| Challenge: | Multilingual neural machine translation models support fine-tuning hundreds of languages simultaneously. |
| Approach: | They propose to fine-tune a language in its intrinsic subspace with a tiny fraction of entire parameters. |
| Outcome: | The proposed methods outperform full-parameter fine-tuning up to 2.25 spBLEU scores and reduce trainable parameters to 0.4% for high and medium-resource languages and 1.6% for low-resourced ones. |
Cross-lingual Contextualized Phrase Retrieval (2024.findings-emnlp)
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| Challenge: | Phrase-level dense retrieval has shown many appealing characteristics in downstream NLP tasks. |
| Approach: | They propose a task formulation of dense retrieval, cross-lingual contextualized phrase retrieval . they extract pairs of cross-linguistic phrases using word alignment information . |
| Outcome: | The proposed task formulation surpasses baselines on the phrase retrieval task and a downstream task, i.e., machine translation, and achieves top-1 accuracy 13 points higher. |
MusTQ: A Temporal Knowledge Graph Question Answering Dataset for Multi-Step Temporal Reasoning (2024.findings-acl)
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| Challenge: | Existing studies focus on fact-centered reasoning with limited attention to temporal reasoning. |
| Approach: | They propose a new TKGQA dataset, MusTQ, which contains 666K multi-step temporal reasoning questions and a TKG. |
| Outcome: | The proposed model achieves state-of-the-art multi-step temporal reasoning ability with entity-time attention mechanism and optimized temporal knowledge graph representation. |