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Found 29 papers, 10 papers with code
A BiRGAT Model for Multi-intent Spoken Language Understanding with Hierarchical Semantic Frames
Previous work on spoken language understanding (SLU) mainly focuses on single-intent settings, where each input utterance merely contains one user intent.
ChemDFM: Dialogue Foundation Model for Chemistry
To this end, we develop ChemDFM, the first LLM towards CGI.
On the Structural Generalization in Text-to-SQL
Exploring the generalization of a text-to-SQL parser is essential for a system to automatically adapt the real-world databases.
OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue
The second phase is to fine-tune the pretrained model on the TOD data.
DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning
In this work, we aim to build a unified dialogue foundation model (DFM) which can be used to solve massive diverse dialogue tasks.
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).
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF
In this paper, we propose to improve prototypical networks with vector projection distance and abstract triangular Conditional Random Field (CRF) for the few-shot NLU.
LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations
This work aims to tackle the challenging heterogeneous graph encoding problem in the text-to-SQL task.
ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser
Given a database schema, Text-to-SQL aims to translate a natural language question into the corresponding SQL query.
LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching
Additionally, we adopt the word lattice graph as input to maintain multi-granularity information.
Dual Learning for Dialogue State Tracking
In task-oriented multi-turn dialogue systems, dialogue state refers to a compact representation of the user goal in the context of dialogue history.
CREDIT: Coarse-to-Fine Sequence Generation for Dialogue State Tracking
In dialogue systems, a dialogue state tracker aims to accurately find a compact representation of the current dialogue status, based on the entire dialogue history.
Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding
Few-shot slot tagging becomes appealing for rapid domain transfer and adaptation, motivated by the tremendous development of conversational dialogue systems.
Robust Spoken Language Understanding with RL-based Value Error Recovery
The framework consists of a slot tagging model and a rule-based value error recovery module.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks
We also adopt graph attention networks with higher-order neighborhood information to encode the rich structure in AMR graphs.
Neural Graph Matching Networks for Chinese Short Text Matching
Chinese short text matching usually employs word sequences rather than character sequences to get better performance.
Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing
One daunting problem for semantic parsing is the scarcity of annotation.
Jointly Encoding Word Confusion Network and Dialogue Context with BERT for Spoken Language Understanding
In this paper, a novel BERT based SLU model (WCN-BERT SLU) is proposed to encode WCNs and the dialogue context jointly.
Dual Learning for Semi-Supervised Natural Language Understanding
The framework is composed of dual pseudo-labeling and dual learning method, which enables an NLU model to make full use of data (labeled and unlabeled) through a closed-loop of the primal and dual tasks.
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking
In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms.
Ranked #8 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks
For multi-domain DST, the data sparsity problem is also a major obstacle due to the increased number of state candidates.
Ranked #12 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
Prior Knowledge Driven Label Embedding for Slot Filling in Natural Language Understanding
The proposed approaches are evaluated on three datasets.
Data Augmentation with Atomic Templates for Spoken Language Understanding
The atomic templates produce exemplars for fine-grained constituents of semantic representations.
Semantic Parsing with Dual Learning
Semantic parsing converts natural language queries into structured logical forms.
A Hierarchical Decoding Model For Spoken Language Understanding From Unaligned Data
In the paper, we focus on spoken language understanding from unaligned data whose annotation is a set of act-slot-value triples.
Concept Transfer Learning for Adaptive Language Understanding
Concept definition is important in language understanding (LU) adaptation since literal definition difference can easily lead to data sparsity even if different data sets are actually semantically correlated.
Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken Language Understanding
This paper investigates the framework of encoder-decoder with attention for sequence labelling based spoken language understanding.
