565 citations · 3k across the 108 of their papers we have counts for
96 papers · 1 filter
Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training
Bo Zheng, Li Dong, Shaohan Huang +5
Compared to monolingual models, cross-lingual models usually require a more expressive vocabulary to represent all languages adequately. We find that many languages are under-repre…
CausalBERT: Injecting Causal Knowledge Into Pre-trained Models with Minimal Supervision
Zhongyang Li, Xiao Ding, Kuo Liao +2
Recent work has shown success in incorporating pre-trained models like BERT to improve NLP systems. However, existing pre-trained models lack of causal knowledge which prevents tod…
Guided Generation of Cause and Effect
Zhongyang Li, Xiao Ding, Ting Liu +2
We present a conditional text generation framework that posits sentential expressions of possible causes and effects. This framework depends on two novel resources we develop in th…
Consistency Regularization for Cross-Lingual Fine-Tuning
Bo Zheng, Li Dong, Shaohan Huang +7
Fine-tuning pre-trained cross-lingual language models can transfer task-specific supervision from one language to the others. In this work, we propose to improve cross-lingual fine…
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data
Haoyu Song, Yan Wang, Kaiyan Zhang +2
Maintaining consistent personas is essential for dialogue agents. Although tremendous advancements have been brought, the limited-scale of annotated persona-dense data are still ba…
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings
Ziqing Yang, Yiming Cui, Chenglei Si +4
Adversarial training (AT) as a regularization method has proved its effectiveness on various tasks. Though there are successful applications of AT on some NLP tasks, the distinguis…
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
Libo Qin, Fuxuan Wei, Tianbao Xie +3
Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention. However, the state-of-the-art joint models heavily rely on autoregressive ap…
Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization
Xiachong Feng, Xiaocheng Feng, Libo Qin +2
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 capabi…
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling
Yutai Hou, Yongkui Lai, Cheng Chen +2
In this paper, we investigate few-shot joint learning for dialogue language understanding. Most existing few-shot models learn a single task each time with only a few examples. How…
ExpMRC: Explainability Evaluation for Machine Reading Comprehension
Yiming Cui, Ting Liu, Wanxiang Che +2
Achieving human-level performance on some of Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs). How…
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension
Jiaqi Li, Ming Liu, Zihao Zheng +4
Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC as models must handle the complex dialogue discourse structure, previously unconsidered in trad…
A Survey on Spoken Language Understanding: Recent Advances and New Frontiers
Libo Qin, Tianbao Xie, Wanxiang Che +1
Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries, which is a core component in a task-oriented dialog system. With the burst of deep neural n…
Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering
Nan Shao, Yiming Cui, Ting Liu +2
Retrieving information from correlative paragraphs or documents to answer open-domain multi-hop questions is very challenging. To deal with this challenge, most of the existing wor…
Multiple Structural Priors Guided Self Attention Network for Language Understanding
Le Qi, Yu Zhang, Qingyu Yin +1
Self attention networks (SANs) have been widely utilized in recent NLP studies. Unlike CNNs or RNNs, standard SANs are usually position-independent, and thus are incapable of captu…
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…
Unsupervised Explanation Generation for Machine Reading Comprehension
Yiming Cui, Ting Liu, Shijin Wang +1
With the blooming of various Pre-trained Language Models (PLMs), Machine Reading Comprehension (MRC) has embraced significant improvements on various benchmarks and even surpass hu…
CharBERT: Character-aware Pre-trained Language Model
Wentao Ma, Yiming Cui, Chenglei Si +3
Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almos…
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…
Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks
Xiachong Feng, Xiaocheng Feng, Bing Qin +1
Abstractive dialogue summarization is the task of capturing the highlights of a dialogue and rewriting them into a concise version. In this paper, we present a novel multi-speaker…
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…
A Compare Aggregate Transformer for Understanding Document-grounded Dialogue
Longxuan Ma, Weinan Zhang, Runxin Sun +1
Unstructured documents serving as external knowledge of the dialogues help to generate more informative responses. Previous research focused on knowledge selection (KS) in the docu…