Search Results for author:
Found 43 papers, 29 papers with code
GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding
Specifically, we employ contrastive learning, leveraging bilingual dictionaries to construct multilingual views of the same utterance, then encourage their representations to be more similar than negative example pairs, which achieves to explicitly align representations of similar sentences across languages.
Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation.
CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue
Consistency identification in task-oriented dialog (CI-ToD) usually consists of three subtasks, aiming to identify inconsistency between current system response and current user response, dialog history and the corresponding knowledge base.
Improving Language Model Reasoning with Self-motivated Learning
The framework motivates the model itself to automatically generate rationales on existing datasets.
Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers
To this end, in this paper, we present 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.
Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction
To promote the CGEC field to better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC.
MultiPoT: Multilingual Program of Thoughts Harnesses Multiple Programming Languages
In this paper, we conduct comprehensive experiments on the programming languages used in PoT and find that no single language consistently delivers optimal performance across all tasks and models.
Pro-HAN: A Heterogeneous Graph Attention Network for Profile-Based Spoken Language Understanding
Recently, Profile-based Spoken Language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i. e., Knowledge Graph, User Profile, Context Awareness) to eliminate the prevalent ambiguities in user utterances.
SDIF-DA: A Shallow-to-Deep Interaction Framework with Data Augmentation for Multi-modal Intent Detection
The two core challenges for multi-modal intent detection are (1) how to effectively align and fuse different features of modalities and (2) the limited labeled multi-modal intent training data.
DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms
DTIAM, for the first time, provides a unified framework for accurate and robust prediction of drug-target interactions, binding affinities, and activation/inhibition mechanisms.
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions
End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity.
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages
The cross-lingual alignment prompting is responsible for aligning representations across different languages, whereas the task-specific solver prompting is used to generate the final chain of thoughts and results for the reasoning task.
Improving Few-shot and Zero-shot Entity Linking with Coarse-to-Fine Lexicon-based Retriever
Few-shot and zero-shot entity linking focus on the tail and emerging entities, which are more challenging but closer to real-world scenarios.
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System
Multi-modal sarcasm detection has attracted much recent attention.
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding
Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e. g., intents and slots).
A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification
Cross-domain text classification aims to adapt models to a target domain that lacks labeled data.
LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model
Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM.
A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding
Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention.
HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understanding
Multilingual spoken language understanding (SLU) consists of two sub-tasks, namely intent detection and slot filling.
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase.
Natural Language Understanding
Out-of-Distribution Generalization
GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding
We present Global--Local Contrastive Learning Framework (GL-CLeF) to address this shortcoming.
UniDU: Towards A Unified Generative Dialogue Understanding Framework
With the development of pre-trained language models, remarkable success has been witnessed in dialogue understanding (DU).
Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding
Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its corresponding semantic frames (e. g., intent and slots).
Ranked #1 on
Semantic Frame Parsing
on ProSLU
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation.
FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark
While different learning schemes -- fine-tuning, zero-shot, and few-shot learning -- have been widely explored and compared for languages such as English, there is comparatively little work in Chinese to fairly and comprehensively evaluate and compare these methods and thus hinders cumulative progress.
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention.
Ranked #1 on
Semantic Frame Parsing
on MixATIS
(Overall Accuracy metric)
Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization
Current dialogue summarization systems usually encode the text with a number of general semantic features (e. g., keywords and topics) to gain more powerful dialogue modeling capabilities.
A Survey on Spoken Language Understanding: Recent Advances and New Frontiers
Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries, which is a core component in a task-oriented dialog system.
Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification
The dialog context information (contextual information) and the mutual interaction information are two key factors that contribute to the two related tasks.
A Co-Interactive Transformer for Joint Slot Filling and Intent Detection
Instead of adopting the self-attention mechanism in vanilla Transformer, we propose a co-interactive module to consider the cross-impact by building a bidirectional connection between the two related tasks.
Injecting Word Information with Multi-Level Word Adapter for Chinese Spoken Language Understanding
In this paper, we improve Chinese spoken language understanding (SLU) by injecting word information.
N-LTP: An Open-source Neural Language Technology Platform for Chinese
We introduce \texttt{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).
DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification
In dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately.
Dialogue State Induction Using Neural Latent Variable Models
Dialogue state modules are a useful component in a task-oriented dialogue system.
Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network
Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG).
CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP
Compared with the existing work, our method does not rely on bilingual sentences for training, and requires only one training process for multiple target languages.
Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization
Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain.
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
However, there has been relatively little research on how to effectively use data from all domains to improve the performance of each domain and also unseen domains.
Ranked #1 on
Task-Oriented Dialogue Systems
on Kvret
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction.
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system.
Ranked #6 on
Task-Oriented Dialogue Systems
on KVRET
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge.
Ranked #2 on
Intent Detection
on SNIPS
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation
Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base.
Ranked #7 on
Task-Oriented Dialogue Systems
on KVRET
