Papers with language and
Cross-Lingual Transfer with Target Language-Ready Task Adapters (2023.findings-acl)
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| Challenge: | Existing frameworks for (zero-shot) cross-lingual transfer employ separate language and task adapters which can be arbitrarily combined to perform any task to any target language. |
| Approach: | They propose to fine-tune 'target language-ready' adapters to the target language to achieve better transfer performance without sacrificing the modularity of MAD-X. |
| Outcome: | The proposed adapters outperform MAD-X and BAD-X on most tasks and languages while maintaining the modularity of MAD. |
RB-LoRA: Rank-Balanced Aggregation for Low-Rank Adaptation with Federated Fine-Tuning (2026.findings-eacl)
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| Challenge: | Low-rank adaptation (LoRA) improves fine-tuning of foundation models by updating only compact adapter matrices . varying client device capabilities lead to different adapter ranks, causing rank heterogeneity that undermines aggregation. |
| Approach: | They propose a rank-balanced aggregation framework that decomposes each update into rank-wise components and aligns them using analytically derived weights. |
| Outcome: | Experiments on language and vision models show that RB-LoRA improves under one and three rounds of communication in federated learning environments. |
Unifying Cross-Lingual Transfer across Scenarios of Resource Scarcity (2023.emnlp-main)
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| Challenge: | Existing approaches to deal with resource scarcity have not been developed to deal effectively with the problem. |
| Approach: | They propose to use a set of tools to harness data from one or more high-resource "source" languages to compensate for a shortage of data in low-resourced "target" languages. |
| Outcome: | The proposed technique can be easily adapted to unseen languages, extending the range of the proposed technique and translation-based transfer more broadly. |
ProsocialDialog: A Prosocial Backbone for Conversational Agents (2022.emnlp-main)
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Hyunwoo Kim, Youngjae Yu, Liwei Jiang, Ximing Lu, Daniel Khashabi, Gunhee Kim, Yejin Choi, Maarten Sap
| Challenge: | Existing dialogue systems fail to respond properly to potentially unsafe user utterances . existing systems either ignore or passively agree with unsafe content . |
| Approach: | They introduce a dataset to teach conversational agents to respond to problematic content following social norms. |
| Outcome: | The proposed dataset shows that ProsocialDialog generates more socially acceptable dialogues than existing models. |
Jejueo Datasets for Machine Translation and Speech Synthesis (2020.lrec-1)
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| Challenge: | Jejueo, or the Jeju language, is a minority language used on Jeju Island . there have been many efforts to revitalize the language, but few computational approaches have been used to solve its problems. |
| Approach: | They construct two new Jejueo datasets using interviews and transcripts . they build machine translation and speech synthesis using these datasets based on their results . |
| Outcome: | The proposed datasets will attract interest of both language and machine learning communities. |
BabyCloud, a Technological Platform for Parents and Researchers (L18-1)
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Xuân-Nga Cao, Cyrille Dakhlia, Patricia Del Carmen, Mohamed-Amine Jaouani, Malik Ould-Arbi, Emmanuel Dupoux
| Challenge: | a platform for capturing, storing and analyzing day-long audio recordings and photos of children's linguistic environments is proposed . the proposed platform connects families and academics, with strong innovation potential for each type of users. |
| Approach: | They propose a platform for capturing, storing and analyzing audio recordings and photos of children's linguistic environments. |
| Outcome: | The proposed platform connects families and academics with strong innovation potential for each type of users. |
Locate-then-Merge: Neuron-Level Parameter Fusion for Mitigating Catastrophic Forgetting in Multimodal LLMs (2025.findings-emnlp)
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| Challenge: | Multimodal instruction tuning often causes catastrophic forgetting of the base LLM’s language ability, even in strong models like Llama3. |
| Approach: | They propose a training-free parameter fusion framework that locates important parameters and selectively merges them. |
| Outcome: | The proposed framework preserves the influence of neurons with large parameter shifts while attenuating the influence . of neurons likely responsible for newly acquired visual capabilities while mitigating language degradation. |
ISAAQ - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention (2020.emnlp-main)
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| Challenge: | Textbook Question Answering is a complex task that requires reasoning with multimodal information from text and diagrams. |
| Approach: | They propose to use transformer language models and bottom-up and top-down attention to tackle the language and visual understanding challenges of text and diagrams. |
| Outcome: | The proposed system achieves unprecedented accuracies on all TQA question types . the system also obtains state-of-the-art results in other demanding datasets . |
Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation (2024.findings-emnlp)
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| Challenge: | Recent neural news recommenders (NNRs) extend content-based recommendation by aligning additional aspects between candidate news and user history or diversifying recommendations w.r.t. these aspects require retraining of the model with a modified objective. |
| Approach: | They introduce a modular framework for multi-aspect neural news recommendation that supports on-the-fly customization over individual aspects at inference time. |
| Outcome: | The proposed framework outperforms state-of-the-art NNRs on both content-based recommendation and single- and multi-aspect customization. |
R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation (2025.emnlp-main)
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| Challenge: | Reasoning is a fundamental capability underpinning text-to-image (T2I) generation. |
| Approach: | They propose a benchmark to rigorously assess reasoning-driven T2I generation. |
| Outcome: | Experiments with 16 representative T2I models show limited reasoning performance . a strong pipeline-based framework decouples reasoning and generation . |
CTC-based Non-autoregressive Speech Translation (2023.acl-long)
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Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu
| Challenge: | End-to-end speech translation (E2E ST) and non-autoregressive (NAR) generation are promising in language and speech processing for their advantages of less error propagation and low latency. |
| Approach: | They develop a model that uses connectionist temporal classification to predict the source and target texts. |
| Outcome: | The proposed model achieves an average BLEU score of 29.5 with a speed-up of 5.67. |
Project MOSLA: Recording Every Moment of Second Language Acquisition (2024.lrec-main)
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| Challenge: | Second language acquisition (SLA) is a complex and dynamic process. |
| Approach: | They created a longitudinal, multimodal, multilingual, and controlled dataset by inviting participants to learn one of three target languages from scratch over a span of two years, exclusively through online instruction. |
| Outcome: | The proposed dataset sheds light on the complex and dynamic nature of the acquisition of a second language and its implications for proficiency assessment, language and speech processing, and multimodal learning analytics. |
GraphAgent: Agentic Graph Language Assistant (2025.emnlp-main)
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| Challenge: | Real-world data combines structured and unstructured formats, capturing explicit relationships and implicit semantic interdependencies. |
| Approach: | They propose GraphAgent, an automated agent pipeline addressing both explicit and implicit graph-enhanced semantic dependencies for predictive and generative tasks. |
| Outcome: | Extensive experiments on diverse datasets validate GraphAgent’s effectiveness in graph-related predictive and text generative tasks. |
From Words to Pixels: A Comprehensive Survey on Large Language Models in Visual Segmentation (2026.acl-long)
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| Challenge: | Visual segmentation with instruction has been a challenging task for many years . large language models and large multimodal models have spurred a new wave of research . |
| Approach: | They review recent works in LLM-based visual segmentation and analyze their architectural innovations, training strategies, and benchmark performance. |
| Outcome: | The present study reviews the most recent works in LLM-driven visual segmentation . it identifies key challenges and promising future directions . |