Papers by Jiatao Li
Meta-Learning for Low-Resource Neural Machine Translation (D18-1)
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| Challenge: | In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm for low-resource neural machine translation (NMT). |
| Approach: | They propose to extend the recently introduced meta-learning algorithm for low-resource neural machine translation (NMT) they frame low-Resource translation as a meta- learning problem where we learn to adapt to low-REsource languages based on multilingual high-resourced language tasks. |
| Outcome: | The proposed meta-learning algorithm outperforms the multilingual, transfer learning based approach and can train a competitive NMT system with only a fraction of training examples. |
Universal Neural Machine Translation for Extremely Low Resource Languages (N18-1)
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| Challenge: | a novel multilingual approach to machine translation is proposed for low resource languages . the proposed approach can achieve 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system . |
| Approach: | They propose a transfer-learning approach to share lexical and sentence representations across multiple source languages into one target language. |
| Outcome: | The proposed approach achieves 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system which uses multi-lingual training and back-translation. |
Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing variational inference models ignore their latent variables, a phenomenon called posterior collapse. |
| Approach: | They propose a new loss function for conditional variational autoencoders that counteracts posterior collapse by using a modified evidence lower bound objective and a factorized decoder. |
| Outcome: | The proposed model yields improved translation quality compared to existing models on WMT RoEn and DeEn. |
Multilingual Denoising Pre-training for Neural Machine Translation (2020.tacl-1)
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Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer
| Challenge: | Existing approaches to pre-train models focus on only English corpora, but this is not common in machine translation. |
| Approach: | They propose a sequence-to-sequence denoising auto-encoder pre-trained on monolingual corpora . they show that it produces significant performance gains across MT tasks . |
| Outcome: | The proposed model can achieve significant performance gains across a wide variety of MT tasks. |
Multilingual Translation from Denoising Pre-Training (2021.findings-acl)
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Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan
| Challenge: | Recent work shows potential of training one model for multilingual machine translation . but little has been explored on the potential to combine denoising pretraining with multilingual translation in a single model. |
| Approach: | They propose to combine denoising pretraining with multilingual machine translation in a single model. |
| Outcome: | The proposed model improves over models trained from scratch and bilingually for translation into English. |
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models (2025.findings-naacl)
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| Challenge: | Recent research focuses on optimizing the use of Self-Docs with their inherent properties remaining underexplored. |
| Approach: | They develop a taxonomy to compare the effectiveness of different types of Self-Docs and explore strategies for combining them with external sources. |
| Outcome: | The proposed model can supplement retrieved content and provide a powerful way to improve knowledge-intensive question answering tasks. |
Who Writes What: Unveiling the Impact of Author Roles on AI-generated Text Detection (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) require accurate text detection, but authors' characteristics are neglected. |
| Approach: | They investigate how author characteristics impact AI-generated text detection . they use corpus of human-authored texts and parallel AI-generated texts . |
| Outcome: | The results show that gender, CEFR proficiency, academic field and language environment influence detector accuracy. |
Fin-STAR: Structure-as-Semantics to Resolve Implicitness in Financial Retrieval (2026.findings-acl)
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Yu Zou, Yan Chen, Lida He, Qi Zhou, Xiaorui Zhou, Aixi Zhong, Yi Wang, Wei Li, Qingyu Wang, Jiatao Li, Wei Gong, Jialei Zeng, Jingmei Zhao, Ke Jiang, Qing Li
| Challenge: | Existing Retrieval-Augmented Generation systems treat structure as a physical navigational skeleton rather than intrinsic semantic knowledge. |
| Approach: | They propose a framework that redefining hierarchy as intrinsic semantics and uses snippets to enrich hierarchical lineage. |
| Outcome: | The proposed framework outperforms state-of-the-art hierarchical and graph-based benchmarks on FinTierQA Gold. |
Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoders (2021.eacl-main)
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| Challenge: | Recent work in multilingual translation has improved translation quality surpassing bilingual baselines using deep transformer models with increased capacity. |
| Approach: | They propose a deep encoder with multiple shallow decoders to reduce inference latency while maintaining translation quality. |
| Outcome: | The proposed model achieves 1.8x speedup on average compared to a standard transformer model with no drop in translation quality. |
Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations (P19-1)
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| Challenge: | Existing approaches to train a multilingual NMT model for low-resource languages are lacking in terms of number of supervised examples. |
| Approach: | They propose to use decoder pre-training and back-translation to solve the degeneracy problem by analyzing spurious correlations between source and decoded sentences. |
| Outcome: | The proposed methods show significant improvement over the pivot-based approach on three challenging multilingual datasets. |
Unified Speech-Text Pre-training for Speech Translation and Recognition (2022.acl-long)
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Yun Tang, Hongyu Gong, Ning Dong, Changhan Wang, Wei-Ning Hsu, Jiatao Gu, Alexei Baevski, Xian Li, Abdelrahman Mohamed, Michael Auli, Juan Pino
| Challenge: | Existing methods to pre-train speech and text use unlabeled data to learn universal feature representations. |
| Approach: | They propose a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. |
| Outcome: | The proposed method achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset and comparable WERs to wav2vec 2.0 on the Librispeech speech recognition task. |