Papers by Mingming Liu
Cross-Lingual Cross-Modal Consolidation for Effective Multilingual Video Corpus Moment Retrieval (2022.findings-naacl)
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| Challenge: | Existing multilingual video corpus moment retrieval methods are based on a two-stream structure. |
| Approach: | They propose a multilingual video corpus moment retrieval task that uses a two-stream structure to generate a query-visual similarity and a subtitle stream exploits the query-subtitle similarity. |
| Outcome: | The proposed method improves accuracy on a large-scale video corpus moment retrieval dataset. |
Adaptive Hyper-parameter Learning for Deep Semantic Retrieval (2023.emnlp-industry)
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| Challenge: | Existing methods for deep semantic retrieval are highly sensitive to hyper-parameters . a novel adaptive metric learning method is proposed to overcome this limitation . |
| Approach: | They propose a method that adaptively obtains hyper-parameters without fixed or extra-trainable hyper-parmeters . they adopt a symmetric metric learning method to mitigate model collapse issues . |
| Outcome: | The proposed method outperforms existing methods on a real-world dataset and brings economic benefits. |
On the Hallucination in Simultaneous Machine Translation (2024.acl-short)
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| Challenge: | Currently, there are no studies which systematically analyze hallucination in SiMT. |
| Approach: | They conduct a comprehensive analysis of hallucination in simultaneous machine translation (SiMT) they find that halluciation is extremely severe, especially as latency increases . |
| Outcome: | The results show that it is possible to alleviate hallucination by decreasing the over usage of target-side information for SiMT. |
MotiR: Motivation-aware Retrieval for Long-Tail Recommendation (2025.acl-industry)
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| Challenge: | Existing methods for retrieval of recommendation systems rely on collaborative filtering signals and lacks similarity for long-tail items. |
| Approach: | They propose a Motivation-aware Retrieval for Long-Tail Recommendation that integrates purchase motivations with traditional item features to capture similarity among long-tail items. |
| Outcome: | The proposed model captures similarity between long-tail items while maintaining collaborative filtering advantages for popular items. |
Classical Sequence Match Is a Competitive Few-Shot One-Class Learner (2022.coling-1)
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| Challenge: | Existing models that use transformers are unable to learn new knowledge in the few-shot scenarios. |
| Approach: | They propose a few-shot one-class problem which takes a known sample as a reference to detect whether an unknown instance belongs to the same class. |
| Outcome: | The proposed method significantly outperforms transformer models under meta-learning and fine-tuning. |
Learning to Translate by Translating: Stabilizing the Dual Loop via Semantic-Aware Self-Evolution (2026.findings-acl)
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Kui Liu, Mingming Yin, Zuoli Tang, Zihao Li, Chilin Fu, Xiaolu Zhang, Jun Zhou, Lixin Zou, Chenliang Li
| Challenge: | Large Language Models (LLMs) have been successful in machine translation, but lack of high-quality parallel corpora and cost constrain scalability. |
| Approach: | They propose an LLM-driven dual-learning framework that enables autonomous translation . they employ a robust semantic-aware reward function that balances adequacy with reconstruction fidelity . |
| Outcome: | The proposed model outperforms larger models on benchmarks and achieves parity with state-of-the-art supervised baselines on mainstream benchmarks. |
Density-Aware Prototypical Network for Few-Shot Relation Classification (2023.findings-emnlp)
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| Challenge: | Existing studies treat NOTA as an extra class and treat it the same as known relations. |
| Approach: | They propose a density-aware prototypical network to treat various instances distinctly . they separate known instances and isolate NOTA instances, respectively . their code will be made public after the paper is accepted . |
| Outcome: | The proposed method outperforms strong baselines with robustness towards different NOTA rates. |
Towards Robust Evidence-Aware Fake News Detection via Improving Semantic Perception (2024.lrec-main)
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| Challenge: | Existing methods lack sufficient semantic perception and are easily blinded by textual expressions. |
| Approach: | They propose a model-agnostic training framework to improve the semantic perception of evidence-aware fake news detection by combining two kinds of data augmentations with synthetic data. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on the extended test set while achieving competitive performance on the original one. |
Mitigating Spurious Correlations via Counterfactual Contrastive Learning (2025.findings-emnlp)
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Fengxiang Cheng, Chuan Zhou, Xiang Li, Alina Leidinger, Haoxuan Li, Mingming Gong, Fenrong Liu, Robert Van Rooij
| Challenge: | Existing methods to distinguish causally related words from spurious correlations are limited by the number of causally correlated words in a sentence. |
| Approach: | They propose to use probabilistic probability of necessity and probability of sufficiency to identify causal relationships rather than spurious correlations between words and class labels. |
| Outcome: | The proposed method is based on a contrastive learning approach name CPNS and is validated on public datasets. |
Rethinking Word-Level Auto-Completion in Computer-Aided Translation (2023.emnlp-main)
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| Challenge: | Existing models for word-level auto-completion (WLAC) do not meet the criterion of good auto-completes. |
| Approach: | They propose a measurable criterion to address the question: what kind of words are good auto-completions? they propose an approach to enhance WLAC performance by promoting adherence to the cri-terion. |
| Outcome: | The proposed approach outperforms the top-performing system submitted to the WLAC shared tasks in WMT2022 while using significantly smaller model sizes. |
Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval (2024.emnlp-industry)
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Zhirui Kuai, Zuxu Chen, Huimu Wang, Mingming Li, Dadong Miao, Wang Binbin, Xusong Chen, Li Kuang, Yuxing Han, Jiaxing Wang, Guoyu Tang, Lin Liu, Songlin Wang, Jingwei Zhuo
| Challenge: | Generative retrieval (GR) is a transformative paradigm in search and recommender systems . however, data sparsity and long-tailed distribution hinder the full utilization of GR . |
| Approach: | They propose a method to reduce the "Hourglass" phenomenon in RQ-SID where codebook tokens become overly concentrated. |
| Outcome: | The proposed methods improve retrieval efficiency and generalization capabilities. |
Context Consistency between Training and Inference in Simultaneous Machine Translation (2024.acl-long)
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| Challenge: | Simultaneous machine translation (SiMT) aims to yield a partial translation with a monotonically growing source-side context. |
| Approach: | They propose a training approach that encourages consistent context usage between training and inference by optimizing translation quality and latency as bi-objectives and exposing the predictions to the model during the training. |
| Outcome: | The proposed system outperforms existing SiMT systems with context inconsistency for the first time. |
MQuinE: a Cure for “Z-paradox” in Knowledge Graph Embedding (2024.emnlp-main)
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| Challenge: | Existing knowledge graph embedding models suffer from Z-paradox, a deficiency in expressiveness . Embedding-based models map each entity and relation into a vector or matrix . |
| Approach: | They propose a new knowledge graph embedding model that does not suffer from Z-paradox while preserves strong expressiveness to model various relation patterns with theoretical justification. |
| Outcome: | The proposed model outperforms existing models on link prediction tasks while maintaining strong expressiveness. |
A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression (2020.emnlp-main)
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| Challenge: | Existing OIE (Open Information Extraction) algorithms are redundant and not reusable. |
| Approach: | They propose a pipeline where an Open-domain Information eXpression task provides a platform for all OIE strategies. |
| Outcome: | The proposed pipeline provides a platform for all OIE strategies. |