Papers by Danni Liu
How Transferable are Attribute Controllers on Pretrained Multilingual Translation Models? (2024.eacl-long)
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| Challenge: | Pretrained multilingual translation models with massive coverage are becoming of the backbone of many translation systems. |
| Approach: | They propose to use a gradient-based inference-time controller to control a pretrained multilingual model by using a model with attribute annotations. |
| Outcome: | The proposed model performs well on pretrained multilingual models and is attribute- rather than language-specific. |
Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach (2024.emnlp-main)
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| Challenge: | Incorrect translation of rare words can severely degrade the accuracy of ST models . |
| Approach: | They propose a retrieval-and-demonstration approach to enhance rare word translation accuracy in ST models by incorporating retrieved examples into ST models. |
| Outcome: | The proposed approach outperforms other modalities and exhibits higher robustness to unseen speakers. |
Improving Zero-Shot Translation by Disentangling Positional Information (2021.acl-long)
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| Challenge: | Multilingual neural machine translation has shown the capability of directly translating between language pairs unseen in training, i.e. zero-shot translation. |
| Approach: | They propose to remove residual connections in an encoder layer to reduce the difficulty of generalizing to new translation directions. |
| Outcome: | The proposed model outperforms pivot-based translation in terms of quality and ease of integration of new languages. |
End-to-End Evaluation for Low-Latency Simultaneous Speech Translation (2023.emnlp-demo)
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Christian Huber, Tu Anh Dinh, Carlos Mullov, Ngoc-Quan Pham, Thai Binh Nguyen, Fabian Retkowski, Stefan Constantin, Enes Ugan, Danni Liu, Zhaolin Li, Sai Koneru, Jan Niehues, Alexander Waibel
| Challenge: | a framework to evaluate low-latency speech translations is currently only limited to specific aspects and is not able to compare different approaches. |
| Approach: | They propose a framework to perform and evaluate low-latency speech translation in realistic conditions. |
| Outcome: | The proposed framework evaluates various aspects of low-latency speech translation under realistic conditions. |
Benchmarking Diffusion Models for Machine Translation (2024.eacl-srw)
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| Challenge: | Diffusion models have shown great potential on many generative tasks, but their application to natural language processing (NLP) is still a less explored direction. |
| Approach: | They adapt two diffusion-based text generation models, Diffusion-LM and DiffuSeq, to perform machine translation. |
| Outcome: | The proposed models struggle more on long-range dependencies than other models. |
Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMs (2025.acl-long)
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| Challenge: | Effective cross-lingual transfer is hindered by performance gaps and the scarcity of fine-tuning data in many languages. |
| Approach: | They propose a middle-layer alignment objective integrated into task-specific training to improve cross-lingual transfer across languages. |
| Outcome: | The proposed method improves cross-lingual transfer to lower-resource languages and can be merged with existing modules without full re-training. |
Language-Independent Representations Improve Zero-Shot Summarization (2024.naacl-short)
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| Challenge: | Pretrained models can be fine tuned on downstream generation tasks, but they can fail in zero-shot conditions. |
| Approach: | They propose query-key finetuning to decouple task-specific knowledge from pretrained models . they propose a variant that more directly enforces language-agnostic representations . |
| Outcome: | The proposed model decouples task-specific knowledge from pretrained language generation abilities. |
PsyAdvisor: A Plug-and-Play Strategy Advice Planner with Proactive Questioning in Psychological Conversations (2025.acl-long)
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| Challenge: | Current psychological LLMs are constrained by passive response mechanisms, limiting their capacity to deploy proactive strategies for psychological counseling. |
| Approach: | They propose a dataset that provides a multi-turn conversation dataset with interpretive labels including strategy decision logic and reaction attribution. |
| Outcome: | The proposed model significantly improves proactive questioning capacity, conversation depth, and response quality. |
How do Multimodal Foundation Models Encode Text and Speech? An Analysis of Cross-Lingual and Cross-Modal Representations (2025.naacl-short)
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| Challenge: | Recent advances in foundation models have sparked growing interest in expanding their text processing capabilities to speech. |
| Approach: | They analyze the model activations from semantically equivalent sentences across languages in the text and speech modalities and examine how text and spoken are represented in recent multimodal foundation models. |
| Outcome: | The proposed models exhibit cross-lingual differences, but are not explicitly trained for modality-agnostic representations. |
SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading (2024.emnlp-main)
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Tu Dinh, Carlos Mullov, Leonard Bärmann, Zhaolin Li, Danni Liu, Simon Reiß, Jueun Lee, Nathan Lerzer, Jianfeng Gao, Fabian Peller-Konrad, Tobias Röddiger, Alexander Waibel, Tamim Asfour, Michael Beigl, Rainer Stiefelhagen, Carsten Dachsbacher, Klemens Böhm, Jan Niehues
| Challenge: | Large Language Models (LLMs) are rapidly developing and are becoming more and more useful in scientific tasks. |
| Approach: | They propose to use LLM-as-a-judge to grade LLMs on SciEx to assess their ability on scientific tasks. |
| Outcome: | The proposed benchmarks show that the LLMs perform decently on free-form exams, achieving 0.948 Pearson correlation with expert grading. |