Papers by Haoyang Ma
Multilingual Agreement for Multilingual Neural Machine Translation (2021.acl-short)
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| Challenge: | Existing models that only use auxiliary languages to encourage multilingual agreement ignore the relationships between different language pairs. |
| Approach: | They propose a multilingual agreement-based method which explicitly models the agreement between different translation directions by randomly substituting some fragments of the source language with their counterpart translations of auxiliary languages. |
| Outcome: | The proposed method improves on the multilingual translation task of 10 language pairs. |
GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator (2023.acl-long)
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Jian Yang, Shuming Ma, Li Dong, Shaohan Huang, Haoyang Huang, Yuwei Yin, Dongdong Zhang, Liqun Yang, Furu Wei, Zhoujun Li
| Challenge: | Existing pre-training methods underutilize the benefits of language understanding for generation. |
| Approach: | They propose a GAN-style model for encoder-decoder pre-training with an auxiliary discriminator. |
| Outcome: | The proposed model outperforms existing pre-trained models and achieves state-of-the-art performance. |
FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging (2025.acl-long)
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Zichen Tang, Haihong E, Ziyan Ma, Haoyang He, Jiacheng Liu, Zhongjun Yang, Zihua Rong, Rongjin Li, Kun Ji, Qing Huang, Xinyang Hu, Yang Liu, Qianhe Zheng
| Challenge: | Compared to existing benchmarks, FinanceReasoning provides three key advancements: (1) credibility; (2) comprehensiveness; (3) numerical precision; (4) complexity; (5) complexity; and (6) complexity. |
| Approach: | They propose a benchmark to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. |
| Outcome: | The proposed benchmark exceeds existing benchmarks in 67.8% of financial concepts and formulas and is credible, comprehensive, and challenging. |
TRIP: Accelerating Document-level Multilingual Pre-training via Triangular Document-level Pre-training on Parallel Data Triplets (2023.findings-emnlp)
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Hongyuan Lu, Haoyang Huang, Shuming Ma, Dongdong Zhang, Wai Lam, Zhaochuan Gao, Anthony Aue, Arul Menezes, Furu Wei
| Challenge: | Existing approaches to multilingual sequence-to-sequence pre-training rely on monolingual corpora and sometimes synthetic document-level bilingual corporata. |
| Approach: | They propose to leverage document-level trilingual parallel corpora to improve sequence-to-sequence multilingual pre-training by using a novel method called Grafting. |
| Outcome: | The proposed method achieves strong state-of-the-art (SOTA) scores on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark. |
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation (2022.naacl-main)
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Yuchen Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang, Rico Sennrich, Ryan Cotterell, Mrinmaya Sachan, Ming Zhou
| Challenge: | Standard evaluation metrics, e.g., BLEU, TER and METEOR, focus on the quality of translations at the sentence level and do not consider discourse-level features. |
| Approach: | They propose to use a metric to take discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans. |
| Outcome: | The proposed metric possesses better selectivity and interpretability at the document-level, and is more sensitive to document- level nuances. |
From Informal to Formal – Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs (2025.acl-long)
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Jialun Cao, Yaojie Lu, Meiziniu Li, Haoyang Ma, Haokun Li, Mengda He, Cheng Wen, Le Sun, Hongyu Zhang, Shengchao Qin, Shing-Chi Cheung, Cong Tian
| Challenge: | Recent studies in formal mathematical reasoning have shown an unstoppable growth trend. |
| Approach: | They constructed 18k high-quality instruction-response pairs across five mainstream formal specification languages and evaluated them against ten open-sourced LLMs. |
| Outcome: | The proposed model compared instruction-response pairs across five formal specification languages and found that the LLMs were good at writing proof segments when given either the code, or the detailed description of proof steps. |
Improving Multilingual Neural Machine Translation with Auxiliary Source Languages (2021.findings-emnlp)
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| Challenge: | Prior work has shown that translating from multiple source languages improves translation quality. |
| Approach: | They propose to exploit multiple source sentences from auxiliary languages to improve multilingual translation in a more common scenario by using synthetic multi-source corpora. |
| Outcome: | Extensive experiments on Chinese/English-Japanese and a large-scale multilingual translation benchmark show that the proposed model outperforms the baseline model significantly by +4.0 BLEU. |