1.2k citations · 2.9k across the 87 of their papers we have counts for
20 papers · 2 filters
Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification
Libo Qin, Zhouyang Li, Wanxiang Che +2
In a dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the ex…
C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling
Yutai Hou, Sanyuan Chen, Wanxiang Che +2
Slot filling, a fundamental module of spoken language understanding, often suffers from insufficient quantity and diversity of training data. To remedy this, we propose a novel Clu…
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
Yang Xu, Yiheng Xu, Tengchao Lv +9
Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-s…
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection
Shaolei Wang, Zhongyuan Wang, Wanxiang Che +1
Most existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice. There have been several proposals to alleviate t…
Few-shot Learning for Multi-label Intent Detection
Yutai Hou, Yongkui Lai, Yushan Wu +2
In this paper, we study the few-shot multi-label classification for user intent detection. For multi-label intent detection, state-of-the-art work estimates label-instance relevanc…
Injecting Word Information with Multi-Level Word Adapter for Chinese Spoken Language Understanding
Dechuan Teng, Libo Qin, Wanxiang Che +2
In this paper, we improve Chinese spoken language understanding (SLU) by injecting word information. Previous studies on Chinese SLU do not consider the word information, failing t…
A Co-Interactive Transformer for Joint Slot Filling and Intent Detection
Libo Qin, Tailu Liu, Wanxiang Che +3
Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system. The two tasks are closely related and the information of one task ca…
N-LTP: An Open-source Neural Language Technology Platform for Chinese
Wanxiang Che, Yunlong Feng, Libo Qin +1
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…
FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding
Yutai Hou, Jiafeng Mao, Yongkui Lai +4
Few-shot learning (FSL) is one of the key future steps in machine learning and has raised a lot of attention. However, in contrast to the rapid development in other domains, such a…
DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification
Libo Qin, Wanxiang Che, Yangming Li +2
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 expl…
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network
Yutai Hou, Wanxiang Che, Yongkui Lai +4
In this paper, we explore the slot tagging with only a few labeled support sentences (a.k.a. few-shot). Few-shot slot tagging faces a unique challenge compared to the other few-sho…
Towards Conversational Recommendation over Multi-Type Dialogs
Zeming Liu, Haifeng Wang, Zheng-Yu Niu +3
We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e…
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
Bo Zheng, Haoyang Wen, Yaobo Liang +5
Natural Questions is a new challenging machine reading comprehension benchmark with two-grained answers, which are a long answer (typically a paragraph) and a short answer (one or…
Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting
Sanyuan Chen, Yutai Hou, Yiming Cui +3
Deep pretrained language models have achieved great success in the way of pretraining first and then fine-tuning. But such a sequential transfer learning paradigm often confronts t…
Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization
Libo Qin, Minheng Ni, Yue Zhang +3
Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain. However, annotating data for each doma…
Revisiting Pre-Trained Models for Chinese Natural Language Processing
Yiming Cui, Wanxiang Che, Ting Liu +3
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further imp…
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
Libo Qin, Xiao Xu, Wanxiang Che +2
Recent studies have shown remarkable success in end-to-end task-oriented dialog system. However, most neural models rely on large training data, which are only available for a cert…
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
Libo Qin, Xiao Xu, Wanxiang Che +1
In real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) models either mainly focused on the sing…
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
Yiming Cui, Ting Liu, Ziqing Yang +5
Owing to the continuous efforts by the Chinese NLP community, more and more Chinese machine reading comprehension datasets become available. To add diversity in this area, in this…
TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing
Ziqing Yang, Yiming Cui, Zhipeng Chen +4
In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing. It works with different neural network models and su…