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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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- Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network30
Iterative Rectification Network (IRN) is proposed for improving general NLG systems to produce both correct and fluent responses and applies a bootstrapping algorithm to sample training candidates and uses reinforcement learning to incorporate discrete reward related to slot inconsistency into training.
- SDIF-DA: A Shallow-to-Deep Interaction Framework with Data Augmentation for Multi-Modal Intent Detection29
A shallow-to-deep interaction framework with data augmentation (SDIF-DA) that can effectively align and fuse multi-modal features by achieving state-of-the-art performance and extensive analyses show that the introduced data augmentation approach can successfully distill knowledge from the large language model.
- ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Models26
This work introduces a novel video reasoning paradigm: Video-Text Interleaved CoT (ViTCoT), which facilitates more intuitive and cognitively aligned reasoning and extensively explores the potential of the ViTCoT paradigm in the video understanding field.
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- Dialogue State Induction Using Neural Latent Variable Models26
This work proposes the task of dialogue state induction, building two neural latent variable models that mine dialogue states automatically from unlabeled customer service dialogue records and shows that the models can effectively find meaningful dialogue states.
- Towards reasoning era: a survey of long chain-of-thought for reasoning large language models25
This survey distinguishes Long CoT from Short CoT and introduces a novel taxonomy to categorize current reasoning paradigms, and explores the key characteristics of Long CoT: deep reasoning, extensive exploration, and feasible reflection, which enable models to handle more complex tasks and produce more efficient, coherent outcomes compared to the shallower Short CoT.
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- Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System23
This paper introduces CI-ToD, a novel dataset for Consistency Identification in Task-oriented Dialog system, and annotates the single label to enable the model to judge whether the system response is contradictory, but also provides more fine-grained labels to encourage model to know what inconsistent sources lead to it.
- Injecting Word Information with Multi-Level Word Adapter for Chinese Spoken Language Understanding23
Experimental results on two Chinese SLU datasets show that the proposed multi-level word adapter can capture useful word information and achieve state-of-the-art performance.
- S 3 Agent: Unlocking the Power of VLLM for Zero-Shot Multi-Modal Sarcasm Detection22
A multi-view agent framework, S3 Agent, is proposed, designed to enhance zero-shot multi-modal sarcasm detection by leveraging three critical perspectives: superficial expression, semantic information, and sentiment expression, to capture different perspectives of sarcastic expressions.
- Towards Event-level Causal Relation Identification22
This work proposes a model that achieves the best performance under both mention- and event-level settings, outperforming a number of strong baselines by at least 2.8% on F1 score.
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- From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence15
This work provides a comprehensive synthesis and practical guide about code LLMs, systematically examining the complete model life cycle from data curation to post-training through advanced prompting paradigms, code pre-training, supervised fine-tuning, reinforcement learning, and autonomous coding agents.
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- Multi-domain Spoken Language Understanding Using Domain- and Task-aware Parameterization15
This article proposes to improve the parameterization of this method by using domain-specific and task-specific model parameters for fine-grained knowledge representation and transfer and shows its transferability when adapting to a new domain with little data, outperforming the prior best model.
- Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits13
Experimental results not only highlight the polarized predictions and biases in LLMs as key directions for future research, but also confirm that soft labels can provide more benefits to other psychological tasks than hard labels.
- Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts13
This paper proposes a task and model agnostic approach called MultiPoT, which achieves comparable or superior performance compared to the best monolingual PoT in almost all tasks across all models and significantly outperforms Python Self-Consistency.
- Divide-Solve-Combine: An Interpretable and Accurate Prompting Framework for Zero-shot Multi-Intent Detection12
By decomposition, DSCP allows the model to track the explicit reasoning process and improve the interpretability, and proposes an interactive divide-solve-combine prompting (Inter-DSCP) to naturally capture the interaction capabilities of large language models.
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- Text Is No More Enough! A Benchmark for Profile-Based Spoken Language Understanding11
This paper introduces a new and important task, Profile-based Spoken Language Understanding (ProSLU), which requires the model that not only relies on the plain text but also the supporting profile information to predict the correct intents and slots, and introduces a large-scale human-annotated Chinese dataset with over 5K utterances.
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- Improving Language Model Reasoning with Self-motivated Learning9
Self-motivated Learning framework motivates the model itself to automatically generate rationales on existing datasets, and trains a reward model with the rank to evaluate the quality of rationales, and improves the performance of reasoning through reinforcement learning.
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- Towards Complex Scenarios: Building End-to-End Task-Oriented Dialogue System across Multiple Knowledge Bases7
This work introduces a KB-over-KB Heterogeneous Graph Attention Network (KoK-HAN) to facilitate model to reason over multiple KBs and empirically shows that the existing single-KB EToDs fail to work on multi-KB settings that require models to reason across various KBs.
- RBF++: Quantifying and Optimizing Reasoning Boundaries Across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning6
This work defines the reasoning boundary (RB) as the maximum limit of CoT performance, and proposes a combination law for RBs, enabling quantitative analysis and offering actionable guidance across various CoT tasks, and introduces a constant assumption, which replaces unmeasurable RBs with scenario-specific constants.
- CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding6
This work presents the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU and introduces a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection.
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- Learning the Boundary of Solvability: Aligning LLMs to Detect Unsolvable Problems5
A multi-domain dataset containing both solvable and unsolvable questions, UnsolvableQA, together with an alignment framework, UnsolvableRL, and a reinforcement learning paradigm that balances objective unsolvability detection with calibrated confidence under capability limits are proposed.
- Noise-Robustness Through Noise: A Framework combining Asymmetric LoRA with Poisoning MoE5
Drawing inspiration from the mixture-of-experts architecture, LoPE strategically integrates a dedicated poisoning expert in an asymmetric LoRA configuration and performs noise injection on the poisoning expert during fine-tuning to enhance its noise discrimination and processing ability.
- TD-GIN: Token-level Dynamic Graph-Interactive Network for Joint Multiple Intent Detection and Slot Filling5
This paper proposes a Token-level Dynamic Graph-Interactive Network (TD-GIN) for joint multiple intent detection and slot filling, where it model the interaction between multiple intents and each token slot in a unified graph architecture.
- EXCGEC: A Benchmark for Edit-Wise Explainable Chinese Grammatical Error Correction4
The task of EXplainable GEC (EXGEC), which focuses on the integral role of correction and explanation tasks, is introduced and a comprehensive evaluation suite is built by leveraging existing automatic metrics and conducting human evaluation experiments to demonstrate the human consistency of the automatic metrics for free-text explanations.
- Improving Consistency Identification in Task-oriented Dialogue Through Multi-Agent Collaboration4
A multi-agent collaboration framework (MAC-CIToD) is introduced to model the interaction across three sub-tasks in CI-ToD, including (1) Full Connection paradigm, (2) Cycle Connection paradigm, and (3) Central Connection paradigm, which effectively builds interaction across QI, HI, and KBI.
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- DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms4
DTIAM, for the first time, provides a unified framework for accurate and robust prediction of drug-target interactions, binding affinities, and activation/inhibition mechanisms between drugs and targets.
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- Beyond Surface Reasoning: Unveiling the True Long Chain-of-Thought Capacity of Diffusion Large Language Models3
Several practical mitigations are proposed, including parallel-oriented prompting, diffusion early stopping, and parallel scaling, to reduce PSC-induced ineffectiveness and inefficiencies.
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- MPCC: A Novel Benchmark for Multimodal Planning with Complex Constraints in Multimodal Large Language Models3
This work formalizes multimodal constraints in planning, provides a rigorous evaluation framework, and highlights the need for advancements in constraint-aware reasoning for real-world MLLM applications.
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- Beware of Reasoning Overconfidence: Pitfalls in the Reasoning Process for Multi-solution Tasks3
This work proposes the cognitive-rigidity hypothesis, which posits that overconfidence arises when the reasoning process prematurely converges on a narrow set of thought paths, and provides tools for assessing the completeness of LLM reasoning and highlights the need to move evaluation beyond single-answer accuracy toward comprehensive exploration.
- MAC-SLU: Multi-Intent Automotive Cabin Spoken Language Understanding Benchmark3
MAC-SLU is introduced, a novel Multi-Intent Automotive Cabin Spoken Language Understanding Dataset, which increases the difficulty of the SLU task by incorporating authentic and complex multi-intent data and a comprehensive benchmark of leading open-source LLMs and LALMs.
- Pro-HAN: A Heterogeneous Graph Attention Network for Profile-based Spoken Language Understanding3
This work introduces a Heterogeneous Graph Attention Network to perform reasoning across multiple Profile information, called Pro-HAN, and establishes a new state-of-the-art on the ProSLU dataset, with an improvement of approximately 8% across all three metrics.
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- Manager: Aggregating Insights From Unimodal Experts in Two-Tower VLMs and MLLMs2
Manager is proposed, a lightweight, efficient and effective plugin that adaptively aggregates insights from different levels of pre-trained unimodal experts to facilitate more comprehensive VL alignment and fusion and significantly boosts the zero-shot performance of LLaVA-OV across different categories of capabilities, images, and resolutions on 20 downstream datasets, whether the multi-grid algorithm is enabled or not.
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- Exploring Multi-Grained Concept Annotations for Multimodal Large Language Models2
The potential of MMGiC to help MLLMs better locate and learn concepts, aligning vision and language at multiple granularities is clearly explored and demonstrated.
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- Exploring Cross-Lingual Latent Transplantation: Mutual Opportunities and Open Challenges1
This paper introduces and investigates cross-lingual latent transplantation, a probing framework which aims to further exploit the model’s internalized multilingual knowledge during inference and examine its effects on the multilingual capability and cultural adaptability of LLMs.
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- MPFToD: a modularized pre-training framework for consistency identification in task-oriented dialogue1
This paper introduces a modularized pre-training framework (MPFToD), which is capable of utilizing large amounts of KB-free dialogues and proposes three pre-training tasks including query response matching pre-training; dialogue history consistent identification pre-training; and KB mask language modeling to enhance different abilities of CI-ToD model.
- Single-to-Multiple: Learning Multiple Intent Detection with Only Single Intent Data1
This work introduces a novel and important task called single-to-multiple intent detection (S2MID) to learn multiple intent detection with only single-intent data, which can greatly alleviate the multi-intent data annotation burden.
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- End-to-End Task-Oriented Dialogue System with Distantly Supervised Knowledge Base Retriever1
This paper introduces an explicit KB retrieval component (KB retriever) into the seq2seq dialogue system and significantly outperforms other sequence-to-sequence based baseline models on both automatic and human evaluation.
- Multi-instance Multi-label Learning by Extreme Learning Machine1
The results show that MIML-ELM tends to achieve better generalization performance at a higher learning speed than SVM, and the comparison of ELM and SVM on M IML.
- S HARING B EYOND D ECISION : Deep Collaboration between Large Language Models via Representation Ensemble–
This work proposes Representation Ensemble (RISE), a novel ensemble framework that enables cross-LLM representation sharing for richer information exchange and introduces a representation alignment method based on relational similarity measures and an orthogonal latent-space transformation.
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- ESAinsTOD: a unified end-to-end schema-aware instruction-tuning framework for task-oriented dialog modeling–
ESAinsTOD, a unified End-to-end Schema-Aware Instruction-tuning framework for general Task-Oriented Dialog modeling, introduces a structured methodology to go beyond simply fine-tuning Large Language Models, enabling flexible adaptation to various dialogue task flows and schemas.
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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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