23 citations · 102 across the 15 of their papers we have counts for
16 papers · 1 filter
UniDU: Towards A Unified Generative Dialogue Understanding Framework
Zhi Chen, Lu Chen, Bei Chen +5
With the development of pre-trained language models, remarkable success has been witnessed in dialogue understanding (DU). However, current DU approaches usually employ independent…
LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching
Boer Lyu, Lu Chen, Su Zhu +1
Chinese short text matching is a fundamental task in natural language processing. Existing approaches usually take Chinese characters or words as input tokens. They have two limita…
CREDIT: Coarse-to-Fine Sequence Generation for Dialogue State Tracking
Zhi Chen, Lu Chen, Zihan Xu +3
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. While previous…
Dual Learning for Dialogue State Tracking
Zhi Chen, Lu Chen, Yanbin Zhao +2
In task-oriented multi-turn dialogue systems, dialogue state refers to a compact representation of the user goal in the context of dialogue history. Dialogue state tracking (DST) i…
Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding
Su Zhu, Ruisheng Cao, Lu Chen +1
Few-shot slot tagging becomes appealing for rapid domain transfer and adaptation, motivated by the tremendous development of conversational dialogue systems. In this paper, we prop…
Robust Spoken Language Understanding with RL-based Value Error Recovery
Chen Liu, Su Zhu, Lu Chen +1
Spoken Language Understanding (SLU) aims to extract structured semantic representations (e.g., slot-value pairs) from speech recognized texts, which suffers from errors of Automati…
Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing
Ruisheng Cao, Su Zhu, Chenyu Yang +5
One daunting problem for semantic parsing is the scarcity of annotation. Aiming to reduce nontrivial human labor, we propose a two-stage semantic parsing framework, where the first…
Jointly Encoding Word Confusion Network and Dialogue Context with BERT for Spoken Language Understanding
Chen Liu, Su Zhu, Zijian Zhao +3
Spoken Language Understanding (SLU) converts hypotheses from automatic speech recognizer (ASR) into structured semantic representations. ASR recognition errors can severely degener…
Dual Learning for Semi-Supervised Natural Language Understanding
Su Zhu, Ruisheng Cao, Kai Yu
Natural language understanding (NLU) converts sentences into structured semantic forms. The paucity of annotated training samples is still a fundamental challenge of NLU. To solve…
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking
Su Zhu, Jieyu Li, Lu Chen +1
Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacl…
Prior Knowledge Driven Label Embedding for Slot Filling in Natural Language Understanding
Su Zhu, Zijian Zhao, Rao Ma +1
Traditional slot filling in natural language understanding (NLU) predicts a one-hot vector for each word. This form of label representation lacks semantic correlation modelling, wh…
Data Augmentation with Atomic Templates for Spoken Language Understanding
Zijian Zhao, Su Zhu, Kai Yu
Spoken Language Understanding (SLU) converts user utterances into structured semantic representations. Data sparsity is one of the main obstacles of SLU due to the high cost of hum…
Semantic Parsing with Dual Learning
Ruisheng Cao, Su Zhu, Chen Liu +2
Semantic parsing converts natural language queries into structured logical forms. The paucity of annotated training samples is a fundamental challenge in this field. In this work,…
A Hierarchical Decoding Model For Spoken Language Understanding From Unaligned Data
Zijian Zhao, Su Zhu, Kai Yu
Spoken language understanding (SLU) systems can be trained on two types of labelled data: aligned or unaligned. Unaligned data do not require word by word annotation and is easier…
Concept Transfer Learning for Adaptive Language Understanding
Su Zhu, Kai Yu
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 actua…
Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken Language Understanding
Su Zhu, Kai Yu
This paper investigates the framework of encoder-decoder with attention for sequence labelling based spoken language understanding. We introduce Bidirectional Long Short Term Memor…