Papers by Mingming Yang
Sentence-Level Agreement for Neural Machine Translation (P19-1)
Copied to clipboard
| Challenge: | Empirical results show that a sentence-level agreement module can significantly improve the performance of neural machine translation (NMT) |
| Approach: | They propose a sentence-level agreement module to minimize the difference between the representation of source and target sentences. |
| Outcome: | Empirical results show the proposed agreement module significantly improves translation performance. |
pEBR: A Probabilistic Approach to Embedding Based Retrieval (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Existing embedding-based retrieval systems rely on heuristic and suboptimal cutoffs for item retrieval. |
| Approach: | They propose a probabilistic Embedding-Based Retrieval framework that learns a shared semantic representation space for both queries and items. |
| Outcome: | The proposed framework improves retrieval precision and recall, and ablation studies show it captures the differences between head-to-tail queries. |
On the Hallucination in Simultaneous Machine Translation (2024.acl-short)
Copied to clipboard
| 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. |
Towards Robust Evidence-Aware Fake News Detection via Improving Semantic Perception (2024.lrec-main)
Copied to clipboard
| 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. |
Rethinking Word-Level Auto-Completion in Computer-Aided Translation (2023.emnlp-main)
Copied to clipboard
| 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. |
Context Consistency between Training and Inference in Simultaneous Machine Translation (2024.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Addressing Entity Translation Problem via Translation Difficulty and Context Diversity (2024.findings-acl)
Copied to clipboard
| Challenge: | Neural machine translation systems often produce inadequate translations for named entities. |
| Approach: | They propose a data augmentation strategy to enhance the accuracy of named entity translation by retraining the target named entity pair. |
| Outcome: | The proposed method improves translation accuracy across test sets and terminology tests. |