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- Ting LiuSuggested from co-authorship
- Wanxiang CheSuggested from co-authorship
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Works104 from public data
- A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding310
A novel framework for SLU to better incorporate the intent information, which further guiding the slot filling is proposed, which achieves the state-of-the-art performance and outperforms other previous methods by a large margin.
- Large Language Models Meet NLP: A Survey179
A unified taxonomy including a unified taxonomy including parameter-frozen paradigm and parameter-tuning paradigm is introduced to offer a unified perspective for understanding the current progress of LLMs in NLP.
- CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP179
A data augmentation framework to generate multi-lingual code-switching data to fine-tune mBERT, which encourages model to align representations from source and multiple target languages once by mixing their context information.
- N-LTP: An Open-source Neural Language Technology Platform for Chinese172
N-LTP, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: lexical analysis (Chinese word segmentation, part-of-speech tagging, and named entity recognition), syntactic parsing (dependency parsing), and semantic parsing (semantic dependency parsing and semantic role labeling).
- A Co-Interactive Transformer for Joint Slot Filling and Intent Detection156
This paper proposes a co-interactive module to consider the cross-impact by building a bidirectional connection between the two related tasks, where slot and intent can be able to attend on the corresponding mutual information.
- A survey of multilingual large language models148
A unified and thorough review of the field, highlighting recent progress and emerging trends in MLLM research, and provides a unified framework to summarize the current progress in MLLMs.
- A Survey on Spoken Language Understanding: Recent Advances and New Frontiers126
This paper gives a thorough review of this research, covering different aspects including a new taxonomy, and provides a new perspective for SLU, including single model vs. joint model, implicit joint modeling vs. pretrained paradigm.
- AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling122
An Adaptive Graph-Interactive Framework for joint multiple intent detection and slot filling is proposed, where an intent-slot graph interaction layer is applied to each token adaptively to model the strong correlation between the slot and intents.
- LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model114
In this work, a novel structure-aware GLM is proposed, fully unleashing the power of syntactic knowledge for UIE, and rich task-adaptive structural bias is learned that greatly resolves the UIE crux, the long-range dependence issue and boundary identifying.
- Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization114
DialoGPT, a pre-trained model for conversational response generation, can be developed as an unsupervised dialogue annotator, which takes advantage of dialogue background knowledge encoded in DialoG PT.
- GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling107
This paper proposes a non-autoregressive model for joint multiple intent detection and slot filling, achieving more fast and accurate performance while being 11.5 times faster.
- NL-Augmenter 🦎 → 🐍 A Framework for Task-Sensitive Natural Language Augmentation105
The efficacy of NL-Augmenter is demonstrated by using its transformations to analyze the robustness of popular language models by finding different models to be differently challenged on different tasks, with quasi-systematic score decreases.
- DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification103
A Deep Co-Interactive Relation Network (DCR-Net) is proposed to explicitly consider the cross-impact and model the interaction between the two tasks by introducing a co-interactive relation layer and employs the Bidirectional Encoder Representation from Transformer in the framework, which can further boost the model's performance in both tasks.
- Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog99
This work investigates methods that can make explicit use of domain knowledge and introduce a shared-private network to learn shared and specific knowledge and proposes a novel Dynamic Fusion Network (DF-Net) which automatically exploit the relevance between the target domain and each domain.
- DTIAM: a unified framework for predicting drug-target interactions, binding affinities and drug mechanisms94
DTIAM achieves substantial performance improvement over other state-of-the-art methods in all tasks, particularly in the cold start scenario, and independent validation demonstrates the strong generalization ability of DTIAM.
- Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification82
Experimental results on two public datasets show that the proposed Co-Interactive Graph Attention Network successfully captures the two sources of information and achieve the state-of-the-art performance.
- Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers70
A thorough review and provide a unified perspective to summarize the recent progress as well as emerging trends in multilingual large language models (MLLMs) literature is presented.
- Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever65
A novel framework which queries the KB in two steps to improve the consistency of generated entities, and introduces a KB retrieval component which explicitly returns the most relevant KB row given a dialogue history.
- A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding58
This work investigates the understanding ability of ChatGPT for zero-shot dialogue understanding tasks including spoken language understanding (SLU) and dialogue state tracking (DST).
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- KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing45
This paper proposes a plug-and-play method called \textit{KVSharer}, which shares the KV cache between layers to achieve layer-wise compression, and discovers a counterintuitive phenomenon: sharing dissimilar KV caches better preserves the model performance.
- N-LTP: A Open-source Neural Chinese Language Technology Platform with Pretrained Models.45
N-LTP is introduced, an open-source Python Chinese natural language processing toolkit supporting five basic tasks: Chinese word segmentation, part-of-speech tagging, named entity recognition, dependency parsing, and semantic dependency parsing and is the first toolkit to support all Chinese NLP fundamental tasks.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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