Papers by Huixing Jiang
Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System (2021.acl-long)
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| Challenge: | Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. |
| Approach: | They introduce a task, Novel Slot Detection, in the task-oriented dialogue system. |
| Outcome: | The proposed task is based on two public NSD datasets and proposes strong baselines . it aims to identify a sequence of tokens and extract semantic constituents from user queries . |
Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder (2021.acl-long)
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| Challenge: | Existing efforts to capture event argument interactions are limited by the argument role type information of contextual entities. |
| Approach: | They propose to capture event argument interactions as a Seq2Seq-like learning problem where a sentence with a specific event trigger is mapped to a sequence of event argument roles. |
| Outcome: | The proposed neural architecture generates argument roles by incorporating contextual entities’ argument role predictions, like a word-by-word text generation process, thereby distinguishing implicit argument distribution patterns within an event more accurately. |
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning (2022.acl-short)
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| Challenge: | Existing methods to find out out-of-domain (OOD) intents do not take prior knowledge of in-domain data into account. |
| Approach: | They propose a disentangled knowledge transfer method to bridge the gap between IND pre-training and OOD clustering by using a unified multi-head contrastive learning framework. |
| Outcome: | The proposed method is able to group new unknown intents into different clusters, enabling future development of the system. |
Syntactic Graph Convolutional Network for Spoken Language Understanding (2020.coling-main)
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| Challenge: | Existing work on slot filling and intent detection builds joint models without prior knowledge of linguistic knowledge. |
| Approach: | They propose a joint model that integrates syntactic structure for learning slot filling and intent detection jointly. |
| Outcome: | The proposed model outperforms existing models on two public benchmark datasets and further improves on slot filling and intent detection. |
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning (2021.acl-short)
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| Challenge: | Existing methods of OOD detection only focus on whether a sample is correctly classified . lack of real OOD examples leads to poor prior knowledge about these unknown intents . |
| Approach: | They propose a supervised contrastive learning objective to minimize intra-class variance . they employ an adversarial augmentation mechanism to obtain pseudo diverse views . |
| Outcome: | The proposed method minimizes intra-class variance by pulling together in-domain intents belonging to the same class and maximizes inter-class variation by pushing apart samples from different classes. |
Slot Transferability for Cross-domain Slot Filling (2021.findings-acl)
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| Challenge: | Existing work on slot filling uses labeled data from source domains to train a model for target domains. |
| Approach: | They propose a model-agnostic Slot Transferability Measure (STM) to evaluate the transferability from a source slot to a target slot. |
| Outcome: | The proposed method outperforms state-of-the-art models on multiple datasets and models. |
Co-VQA : Answering by Interactive Sub Question Sequence (2022.findings-acl)
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| Challenge: | Existing approaches to Visual Question Answering (VQA) answer questions directly, but people usually decompose a complex question into a sequence of simple sub questions. |
| Approach: | They propose a conversation-based VQA framework that decomposes questions into sub questions and answers them one-by-one. |
| Outcome: | The proposed framework achieves state-of-the-art on VQA 2.0 and VQA-CP v2 datasets. |
Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization (2022.naacl-main)
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| Challenge: | Existing methods for domain adaptation of abstractive dialogue summarization lack generalization ability on new domains. |
| Approach: | They propose a domain-oriented prefix-tuning model that uses a prefix module to alleviate domain entanglement and discrete prompts to guide the model to focus on key contents of dialogues. |
| Outcome: | The proposed model can be generalized to two multi-domain dialogue summarization datasets. |
KG-Adapter: Enabling Knowledge Graph Integration in Large Language Models through Parameter-Efficient Fine-Tuning (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are criticized for lack of expertise and knowledge conflict . KG-Adapter is a parameter-level KG integration method for decoder-only LLMs . |
| Approach: | They propose a parameter-level KG integration method based on parameter-efficient fine-tuning . they use KG-Adapter to integrate knowledge graphs with LLMs and perform joint reasoning . |
| Outcome: | The proposed method outperforms the current state-of-the-art method on four datasets for two different tasks. |
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold (2022.naacl-main)
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Yanan Wu, Keqing He, Yuanmeng Yan, QiXiang Gao, Zhiyuan Zeng, Fujia Zheng, Lulu Zhao, Huixing Jiang, Wei Wu, Weiran Xu
| Challenge: | Existing methods for OOD detection are based on labeled in-domain data . detecting out-of-domain (OOD) or unknown intents is challenging . |
| Approach: | They propose a novel reassigned contrastive learning method to discriminate IND intents for over-confident OOD and an adaptive class-dependent local threshold mechanism to separate similar IND and OOD intents. |
| Outcome: | The proposed method is effective for both aspects of overconfidence issues. |
Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models (2025.acl-long)
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| Challenge: | Existing approaches to mitigate catastrophic forgetting can be broadly categorized into data-based, architecture-based and learning-based methods. |
| Approach: | They propose a subspace regularization method on LoRA structure that imposes constraints on direction of updating matrix’s null space. |
| Outcome: | The proposed method reduces scale of output change while introducing minimal constraint on model capacity. |
Task-Oriented Clustering for Dialogues (2021.findings-emnlp)
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| Challenge: | Existing methods for task-oriented dialogue clustering are difficult to apply directly due to inherent differences between them. |
| Approach: | They propose a Dialogue Task Clustering Network model for task-oriented clustering . they use context-aware utterance representations and cross-dialogue utterrance cluster representations . |
| Outcome: | The proposed model outperforms baselines on three public datasets on all metrics. |