Papers by Shaoxiong Ji
MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki (2024.eacl-demo)
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Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh, Michele Boggia, Ona De Gibert, Shaoxiong Ji, Niki Andreas Loppi, Alessandro Raganato, Raúl Vázquez, Jörg Tiedemann
| Challenge: | a growing trend towards modularization is limiting the size and information that can be handled in large language models. |
| Approach: | They propose a framework for training massively multilingual modular machine translation systems at scale. |
| Outcome: | The proposed framework is adapted to train multilingual models at scale on NVIDIA GPUs. |
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning (2023.eacl-main)
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| Challenge: | Recent advances apply artificial intelligence to predict clinical events or infer the probable diagnosis for clinical decision support. |
| Approach: | They propose a hypernetwork-based approach that generates task-conditioned parameters and coefficients of multitask prediction heads to learn task-specific prediction and balance the multitask learning. |
| Outcome: | Experiments on clinical notes from the real-world MIMIC database show that the proposed model can achieve better performance than baselines and improve zero-shot prediction on unseen diagnoses. |
A Comparison of Language Modeling and Translation as Multilingual Pretraining Objectives (2024.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) display impressive performances and have captured the attention of the NLP community. |
| Approach: | They propose to compare multilingual pretraining objectives in a controlled methodological environment with multilingual models. |
| Outcome: | The proposed model outperforms existing models in 6 languages and demonstrates that multilingual translation is an effective pretraining objective under the right conditions. |
Medical Code Assignment with Gated Convolution and Note-Code Interaction (2021.findings-acl)
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| Challenge: | Medical code assignment from clinical text is a longstanding challenge due to lengthy semantic information in medical notes. |
| Approach: | They propose a method to capture the semantic information of medical notes and a note-code interaction to automate medical code assignment from clinical text. |
| Outcome: | The proposed method outperforms state-of-the-art models on real-world clinical datasets and is on par with light-weighted baselines. |
A New Massive Multilingual Dataset for High-Performance Language Technologies (2024.lrec-main)
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Ona de Gibert, Graeme Nail, Nikolay Arefyev, Marta Bañón, Jelmer van der Linde, Shaoxiong Ji, Jaume Zaragoza-Bernabeu, Mikko Aulamo, Gema Ramírez-Sánchez, Andrey Kutuzov, Sampo Pyysalo, Stephan Oepen, Jörg Tiedemann
| Challenge: | a new massive multilingual dataset is available for language modeling and machine translation training. |
| Approach: | They present a massive multilingual dataset using web crawls from the Internet Archive and CommonCrawl . they use open-source software tools and high-performance computing to acquire, manage and process large corpora . |
| Outcome: | The HPLT language resources is a massive multilingual dataset . it includes monolingual and bilingual corpora extracted from CommonCrawl and the Internet Archive . the results are published online at the journal journal cense4 . |
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare (2022.lrec-1)
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| Challenge: | Existing pretrained language models for mental health detection are inadequate . one in four people worldwide suffers from mental disorders . |
| Approach: | They train and release two pretrained masked language models to benefit machine learning for mental healthcare research . they demonstrate that language representations pretrained in the target domain improve the performance of mental health detection tasks. |
| Outcome: | The proposed models improve mental health detection tasks on several benchmarks and are available for free. |
Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca (2024.findings-eacl)
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| Challenge: | Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants. |
| Approach: | They employ a dataset and machine translations of it to form multilingual data and use it to tune LLMs. |
| Outcome: | The proposed model is on par or better than a model for each language, and multilingual tuning with downsampled data is as powerful and robust. |
You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations (2026.findings-acl)
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Hongbin Na, Zimu Wang, Zhaoming Chen, Peilin Zhou, Yining Hua, Grace Ziqi Zhou, Haiyang Zhang, Tao Shen, Wei Wang, John Torous, Shaoxiong Ji, Ling Chen
| Challenge: | Psychological defenses are strategies people use to manage distress. |
| Approach: | They propose a dialogue corpus with help seeker utterances labeled for defense level and a DMRS Co-Pilot pipeline that provides evidence-based pre-annotations. |
| Outcome: | The proposed framework reduces annotation time by 24.0% in a counterbalanced study. |
Towards Interpretable Mental Health Analysis with Large Language Models (2023.emnlp-main)
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| Challenge: | Existing studies on large language models lack adequate evaluations and prompting strategies for explainability. |
| Approach: | They evaluate the mental health analysis and emotional reasoning ability of large language models (LLMs) using 11 datasets across 5 tasks. |
| Outcome: | The proposed model shows strong in-context learning ability but still has a significant gap with advanced task-specific methods. |
Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing (2022.findings-emnlp)
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| Challenge: | Suicide is a global problem, with one suicide case for every 100 deaths worldwide . social networking sites are an essential forum for communication and information sharing . |
| Approach: | This paper compares natural language processing to suicidal ideation detection and risk assessment . it urges better intention understanding for reliable suicide risk assessment with computational methods . |
| Outcome: | This paper compares the performance of natural language processing to suicidal ideation detection and risk assessment tasks. |
Speculative Decoding for Multi-Sample Inference (2025.findings-emnlp)
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Yiwei Li, Jiayi Shi, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Yueqi Zhang, Ji Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li
| Challenge: | Speculative decoding method exploits consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or external databases. |
| Approach: | They propose a speculative decoding method that exploits the consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or external databases. |
| Outcome: | The proposed method exploits the intrinsic consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or databases. |
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection (2024.lrec-main)
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| Challenge: | Recent studies have used word embedding and deep learning to automate ADE detection from text, but they did not incorporate explicit medical knowledge about drugs and adverse reactions or the corresponding feature learning. |
| Approach: | They propose to integrate medical knowledge into ADE detection from text . they use contextualized embeddings from pretrained language models and convolutional graph neural networks to learn features differently for different types of nodes in the graph. |
| Outcome: | The proposed model outperforms existing models on four public datasets and shows that it is based on medical knowledge and embeddings from pretrained language models and neural networks. |
How Many Languages Make Good Multilingual Instruction Tuning? A Case Study on BLOOM (2025.coling-main)
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| Challenge: | Many large language models (LLMs) support many languages, while others only support a few, e.g. the Llama series. |
| Approach: | They present a case study on BLOOM to understand three pertinent factors affecting performance: the number of languages, language exposure, and similarity between training and test languages. |
| Outcome: | The proposed model can be used to perform multilingual tasks on 1 to 52 languages. |
Test-Time Scaling of Reasoning Models for Machine Translation (2026.eacl-long)
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| Challenge: | Using TTS, Reasoning Models (RMs) are able to perform tasks such as math and coding with limited results. |
| Approach: | They evaluate 12 Reasoning Models across a diverse suite of MT benchmarks, examining three scenarios: direct translation, forced-reasoning extrapolation, and post-editing. |
| Outcome: | The proposed approach improves translation quality on three domains, with inconsistent results for general-purpose RMs and performance plateauing. |
Revisiting Self-Consistency from Dynamic Distributional Alignment Perspective on Answer Aggregation (2025.findings-acl)
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Yiwei Li, Ji Zhang, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Jiayi Shi, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li
| Challenge: | Existing studies on self-consistency show that it improves reasoning abilities by aggregating diverse stochastic samples. |
| Approach: | They propose a confidence-driven mechanism that dynamically calibrates temperature to align with high probability modes. |
| Outcome: | The proposed method outperforms fixed-diversity baselines on reasoning tasks and improves both average and best-case performance. |
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning? (2024.lrec-main)
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| Challenge: | Existing models that pretrain for cross-lingual tasks do not improve cross-linguistic learning. |
| Approach: | They propose to employ machine translation as a continued training objective to enhance language representation learning by bridging multilingual pretraining and cross-lingual applications. |
| Outcome: | The proposed model performance is compared with existing models and their latent representations. |
GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models (2025.emnlp-demos)
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Hengyu Luo, Zihao Li, Joseph Attieh, Sawal Devkota, Ona de Gibert, Xu Huang, Shaoxiong Ji, Peiqin Lin, Bhavani Sai Praneeth Varma Mantina, Ananda Sreenidhi, Raúl Vázquez, Mengjie Wang, Samea Yusofi, Fei Yuan, Jörg Tiedemann
| Challenge: | Existing evaluation frameworks focus on English and a handful of high-resource languages, thereby overlooking the realistic performance of large language models in multilingual and lower-resourced scenarios. |
| Approach: | They propose a unified and lightweight framework that integrates 27 benchmarks under a standard ISO 639-3 language identifier system to enable seamless incorporation of new benchmarks. |
| Outcome: | The proposed framework integrates 27 benchmarks under a standard ISO 639-3 language identifier system, allowing for seamless incorporation of new benchmarks. |
Data-Centric Continual Pre-training for 500+ Languages: A New Bilingual Translation Corpus and Multilingual Models (2026.findings-acl)
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| Challenge: | Large language models pre-trained on massive data have promoted multilingual natural language processing (NLP). |
| Approach: | They construct a bilingual translation corpus with 2,500 language pairs and develop a suite of four models with parallel data. |
| Outcome: | The proposed model suites are evaluated across 7 tasks and 12 benchmarks. |