Papers by Zhaojiang Lin
Personalizing Dialogue Agents via Meta-Learning (P19-1)
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| Challenge: | Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency. |
| Approach: | They propose to extend Model-Agnostic Meta-Learning (MAML) to personalized dialogue learning without using persona descriptions. |
| Outcome: | The proposed model outperforms baseline models in terms of human-evaluated fluency and consistency on a persona-chat dataset. |
Zero-Shot Dialogue State Tracking via Cross-Task Transfer (2021.emnlp-main)
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Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Zhenpeng Zhou, Paul Crook, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, Pascale Fung
| Challenge: | Existing approaches to training a dialogue state tracking model require extensive annotated dialogue data. |
| Approach: | They propose to transfer cross-task knowledge from general question answering corpora to QA model that can handle zero-shot DST. |
| Outcome: | The proposed model improves existing zero-shot and few-shot results on MultiWoz and shows better generalization ability in unseen domains. |
MoEL: Mixture of Empathetic Listeners (D19-1)
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| Challenge: | Neural network approaches for conversation models have shown to be successful in generating fluent and relevant responses. |
| Approach: | They propose a novel end-to-end approach for modeling empathy in dialogue systems by using Mixture of Empathetic Listeners (MoEL). |
| Outcome: | The proposed model outperforms multitask training baseline in terms of empathy, relevance, and fluency. |
VideoMind: Thinking in Steps for Long Video Understanding (2026.eacl-industry)
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Shubhang Bhatnagar, Renxiong Wang, Kapil Krishnakumar, Adel Ahmadyan, Zhaojiang Lin, Lambert Mathias, Xin Luna Dong, Babak Damavandi, Narendra Ahuja, Seungwhan Moon
| Challenge: | Multimodal Large Language Models struggle with Long Video Understanding due to their limited context window and the distributed nature of salient information across many redundant frames. |
| Approach: | They propose a training framework that mimics a human reasoning process to train Long Video Understanding models. |
| Outcome: | The proposed framework achieves 77.6% performance on Video MME, LongVideo, and MLVU benchmarks while yielding 5% improvement on Llama 4 Scout. |
Continual Dialogue State Tracking via Example-Guided Question Answering (2023.emnlp-main)
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Hyundong Cho, Andrea Madotto, Zhaojiang Lin, Khyathi Chandu, Satwik Kottur, Jing Xu, Jonathan May, Chinnadhurai Sankar
| Challenge: | Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services causes catastrophic forgetting. |
| Approach: | They propose to reformulate dialogue state tracking (DST) as a bundle of example-guided question answering tasks to minimize the task shift between services. |
| Outcome: | The proposed model achieves state-of-the-art performance on DST continual learning metrics without relying on any complex regularization or parameter expansion methods. |
Large Language Models as Zero-shot Dialogue State Tracker through Function Calling (2024.acl-long)
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Zekun Li, Zhiyu Chen, Mike Ross, Patrick Huber, Seungwhan Moon, Zhaojiang Lin, Xin Dong, Adithya Sagar, Xifeng Yan, Paul Crook
| Challenge: | Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts. |
| Approach: | They propose a method for solving dialogue state tracking (DST) with large language models through function calling. |
| Outcome: | The proposed approach improves zero-shot DST, allowing adaptation to diverse domains without extensive data collection or model tuning. |
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM (2024.findings-emnlp)
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Jielin Qiu, Andrea Madotto, Zhaojiang Lin, Paul Crook, Yifan Xu, Babak Damavandi, Xin Dong, Christos Faloutsos, Lei Li, Seungwhan Moon
| Challenge: | Vision-extended LLMs have made significant strides in VQA, but they still encounter significant difficulties in handling queries involving long-tail entities. |
| Approach: | They propose a benchmark to test models' ability to identify entities and provide detailed, entity-specific knowledge by combining 10 images and 10 knowledge-intensive QA pairs. |
| Outcome: | The proposed model outperforms existing methods on the SnapNTell dataset, achieving a 66.5% improvement in the BELURT score. |
Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning (2026.eacl-industry)
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Minseok Kim, Jingxiang Chen, Seong-Gyun Leem, Yin Huang, Rashi Rungta, Zhicheng Ouyang, Haibin Wu, Surya Teja Appini, Ankur Bansal, Yang Bai, Yue Liu, Florian Metze, Ahmed A Aly, Anuj Kumar, Ariya Rastrow, Zhaojiang Lin
| Challenge: | Using paralinguistic cues is challenging for speech large language models, authors say . limited training data, annotation difficulty, and models exploiting lexical shortcuts are challenges . a recent study shows that modeling paralinguistic reasoning with multitask RL improves paralinguistics understanding . |
| Approach: | They propose multi-task reinforcement learning with chain-of-thought prompting that elicits explicit affective reasoning. |
| Outcome: | The proposed model improves paralinguistics understanding over baselines and strong proprietary models by 8-12% on Expresso, IEMOCAP, and RAVDESS. |
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking (2021.naacl-main)
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Zhaojiang Lin, Bing Liu, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Andrea Madotto, Eunjoon Cho, Rajen Subba
| Challenge: | Existing models for zero-shot cross-domain dialogue state tracking require in-domain data to model a new domain. |
| Approach: | They propose a slot descriptions enhanced generative approach for zero-shot cross-domain DST by encoding a dialogue context and a slots with a pre-trained encoder and generating slot value in auto-regressive manner. |
| Outcome: | The proposed model significantly improves state-of-the-art results in zero-shot cross-domain setting. |
Getting To Know You: User Attribute Extraction from Dialogues (2020.lrec-1)
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| Challenge: | a new method to extract user attributes from dialogues is needed to improve user understanding. |
| Approach: | They propose to leverage dialogues with conversational agents to automatically extract user attributes from dialogues. |
| Outcome: | The proposed model surpasses retrieval and generation baselines on human evaluation. |
MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems (2020.emnlp-main)
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| Challenge: | Existing approaches to learn dialogue state tracking and response generation are time-intensive and not transferable between domains. |
| Approach: | They propose a transfer learning framework that allows efficient dialogue state tracking with a minimal generation length. |
| Outcome: | The proposed framework improves the inference efficiency and improves state-of-the-art results on multi-domain multi-tasking systems. |
Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs? (2026.eacl-long)
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Kai Sun, Yin Huang, Srishti Mehra, Mohammad Kachuee, Xilun Chen, Renjie Tao, Zhaojiang Lin, Andrea Jessee, Nirav Shah, Alex L Betty, Yue Liu, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
| Challenge: | Existing literature on knowledge extraction for question answering questions whether it is still relevant for question answerrs. |
| Approach: | They extend an existing benchmark with knowledge extraction annotations and evaluate commercial and open-source LLMs of varying sizes. |
| Outcome: | The proposed model can achieve high QA accuracy, but can still benefit from knowledge extraction through augmentation with extracted triples and multi-task learning. |
Introducing Semantics into Speech Encoders (2023.acl-long)
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Derek Xu, Shuyan Dong, Changhan Wang, Suyoun Kim, Zhaojiang Lin, Bing Liu, Akshat Shrivastava, Shang-Wen Li, Liang-Hsuan Tseng, Guan-Ting Lin, Alexei Baevski, Hung-yi Lee, Yizhou Sun, Wei Wang
| Challenge: | Existing self-supervised speech encoders contain primarily acoustic rather than semantic information. |
| Approach: | They propose a task-agnostic unsupervised way to incorporate semantic information from large language model (LLM) systems into self-supervised speech encoders without labeled audio transcriptions. |
| Outcome: | The proposed approach improves spoken language understanding (SLU) performance by over 5% on intent classification (IC), with modest gains in named entity resolution (NER) and slot filling (SF), and spoken question answering (SQA) score by over 22%. |
IMU2CLIP: Language-grounded Motion Sensor Translation with Multimodal Contrastive Learning (2023.findings-emnlp)
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| Challenge: | Existing methods to align motion sensors with text and video are limited in their scale and limited in the use of IMU models. |
| Approach: | They propose to project IMU motion sensor recordings into the joint representation space of Contrastive Language-Image Pre-training (CLIP) they introduce several new IMU-based Wearable AI applications such as motion-based media search, or an LM-based multimodal reasoning with motion sensor data. |
| Outcome: | The proposed approach significantly improves downstream performance when fine-tuned for each application, demonstrating its universal usage as a new pre-trained resource. |
Proactive Assistant Dialogue Generation from Streaming Egocentric Videos (2025.emnlp-main)
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Yichi Zhang, Xin Luna Dong, Zhaojiang Lin, Andrea Madotto, Anuj Kumar, Babak Damavandi, Joyce Chai, Seungwhan Moon
| Challenge: | Recent advances in conversational AI have been substantial, but developing real-time tasks guidance systems remains a challenge. |
| Approach: | They propose a data curation pipeline that synthesizes dialogues from annotated egocentric videos and a suite of automatic evaluation metrics that validated through extensive human studies. |
| Outcome: | The proposed framework synthesizes dialogues from annotated egocentric videos and validates them through extensive human studies. |
Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition (D19-1)
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| Challenge: | Existing work on name-switching focuses on word-level aspects but neglects subword-level characteristics shared across languages. |
| Approach: | They propose hierarchical meta-Embeddings that combine word-level and subword-level embeddings to create language-agnostic lexical representations. |
| Outcome: | The proposed model achieves state-of-the-art in English-Spanish code-switching scenarios. |
Meta-Transfer Learning for Code-Switched Speech Recognition (2020.acl-main)
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| Challenge: | Increasing number of people in the world today speak a mixed-language as a result of being multilingual. |
| Approach: | They propose a method to transfer learn on a code-switched speech recognition system by extracting information from high-resource monolingual datasets. |
| Outcome: | The proposed model outperforms baselines on speech recognition and language modeling tasks and is faster to converge. |
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model (2024.emnlp-industry)
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Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Tushar Nagarajan, Matt Smith, Shashank Jain, Chun-Fu Yeh, Prakash Murugesan, Peyman Heidari, Yue Liu, Kavya Srinet, Babak Damavandi, Anuj Kumar
| Challenge: | Prior work on LLMs focused on models that combine text and one other modality, such as image encoders or proprietary models that are not open sourced. |
| Approach: | They propose a unified model that reasons over diverse input modality signals and generates textual responses. |
| Outcome: | The proposed model performs better on multimodal tasks than industry-leading models . |
Plug-and-Play Conversational Models (2020.findings-emnlp)
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| Challenge: | Large conversational models that generate coherent and fluent responses often require large dialogue datasets. |
| Approach: | They propose and evaluate plug-and-play methods for controllable response generation . they demonstrate a high degree of control over the generated conversational responses . |
| Outcome: | The proposed method does not require further computation at decoding time and does not need fine-tuning of a large language model. |
Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning (2020.findings-emnlp)
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| Challenge: | Large-scale language models can be fine-tuned to learn highly transferable embedding, but they are expensive and require multiple model parameters. |
| Approach: | They propose a way to fine-tune multiple down-stream generation tasks simultaneously using a single, large pretrained model. |
| Outcome: | The proposed model can maintain or improve the performance of fine-tuning the whole model. |
Continual Learning in Task-Oriented Dialogue Systems (2021.emnlp-main)
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Andrea Madotto, Zhaojiang Lin, Zhenpeng Zhou, Seungwhan Moon, Paul Crook, Bing Liu, Zhou Yu, Eunjoon Cho, Pascale Fung, Zhiguang Wang
| Challenge: | Existing continuous learning systems are not designed to add new domains and functionalities through time without incurring the high cost of retraining the whole system. |
| Approach: | They propose a first-ever continual learning benchmark for task-oriented dialogue systems . they propose 'architecture' method based on residual adapters to implement continual training . |
| Outcome: | The proposed architectural method performs better than multitask learning while being 20X faster in learning new domains. |
Extrapolating to Unknown Opinions Using LLMs (2025.coling-main)
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| Challenge: | ice cream flavors and climate change are among the topics people hold on various topics. |
| Approach: | They propose to use a large language model to extrapolate from stances to unknown opinions by prompting and fine-tuning data to improve their ability to extrapole from known to unknown stance. |
| Outcome: | The proposed model can extrapolate from opinions on known topics to unknown ones and generate reasoning behind extrapolation. |
Cross-lingual Spoken Language Understanding with Regularized Representation Alignment (2020.emnlp-main)
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| Challenge: | despite promising results, current cross-lingual models suffer from imperfect cross-linguistic representation alignments between the source and target languages, which makes the performance sub-optimal. |
| Approach: | They propose a regularization approach to align word-level and sentence-level representations across languages without external resources. |
| Outcome: | The proposed model outperforms state-of-the-art models in few-shot and zero-shot scenarios and achieves comparable performance to supervised training with all training data. |
Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems (2020.findings-emnlp)
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Andrea Madotto, Samuel Cahyawijaya, Genta Indra Winata, Yan Xu, Zihan Liu, Zhaojiang Lin, Pascale Fung
| Challenge: | End-to-end systems rely on dialogue state tracking and annotations to fulfill user requests . modularized systems require multiple steps, including a direct interaction with the KB . |
| Approach: | They propose a method to embed the KB directly into the model parameters . they evaluate five task-oriented dialogue datasets with small, medium, and large KBs . |
| Outcome: | The proposed model can embed the KB directly into the model parameters without any DST or template responses, nor the kb as input. |