1.2k citations · 2.9k across the 87 of their papers we have counts for
14 papers · 2 filters
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
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…
Data Augmentation Approaches in Natural Language Processing: A Survey
Bohan Li, Yutai Hou, Wanxiang Che
As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced…
Discovering Drug-Target Interaction Knowledge from Biomedical Literature
Yutai Hou, Yingce Xia, Lijun Wu +6
The Interaction between Drugs and Targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedic…
Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Libo Qin, Tianbao Xie, Shijue Huang +3
Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation. However, in contrast to the r…
DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation
Zeming Liu, Haifeng Wang, Zheng-Yu Niu +2
In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and…
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…
Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models
Yiming Cui, Wei-Nan Zhang, Wanxiang Che +3
Achieving human-level performance on some of the Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).…
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…
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…
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual Transfer
Ziqing Yang, Wentao Ma, Yiming Cui +3
Multilingual pre-trained models have achieved remarkable performance on cross-lingual transfer learning. Some multilingual models such as mBERT, have been pre-trained on unlabeled…
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…
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…