147 citations · 593 across the 36 of their papers we have counts for
36 papers · 1 filter
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN
Shuhe Wang, Yuxian Meng, Rongbin Ouyang +4
To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL mode…
NN-NER: Named Entity Recognition with Nearest Neighbor Search
Shuhe Wang, Xiaoya Li, Yuxian Meng +4
Inspired by recent advances in retrieval augmented methods in NLP~\citep{khandelwal2019generalization,khandelwal2020nearest,meng2021gnn}, in this paper, we introduce a nearest…
Faster Nearest Neighbor Machine Translation
Shuhe Wang, Jiwei Li, Yuxian Meng +5
NN based neural machine translation (NN-MT) has achieved state-of-the-art results in a variety of MT tasks. One significant shortcoming of NN-MT lies in its inefficiency i…
Triggerless Backdoor Attack for NLP Tasks with Clean Labels
Leilei Gan, Jiwei Li, Tianwei Zhang +6
Backdoor attacks pose a new threat to NLP models. A standard strategy to construct poisoned data in backdoor attacks is to insert triggers (e.g., rare words) into selected sentence…
Interpreting Deep Learning Models in Natural Language Processing: A Review
Xiaofei Sun, Diyi Yang, Xiaoya Li +6
Neural network models have achieved state-of-the-art performances in a wide range of natural language processing (NLP) tasks. However, a long-standing criticism against neural netw…
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models
Kangjie Chen, Yuxian Meng, Xiaofei Sun +4
Pre-trained Natural Language Processing (NLP) models can be easily adapted to a variety of downstream language tasks. This significantly accelerates the development of language mod…
GNN-LM: Language Modeling based on Global Contexts via GNN
Yuxian Meng, Shi Zong, Xiaoya Li +4
Inspired by the notion that ``{\it to copy is easier than to memorize}``, in this work, we introduce GNN-LM, which extends the vanilla neural language model (LM) by allowing to ref…
OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts
Shuhe Wang, Yuxian Meng, Xiaoya Li +3
In order to better simulate the real human conversation process, models need to generate dialogue utterances based on not only preceding textual contexts but also visual contexts.…
An MRC Framework for Semantic Role Labeling
Nan Wang, Jiwei Li, Yuxian Meng +5
Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument label…
Paraphrase Generation as Unsupervised Machine Translation
Xiaofei Sun, Yufei Tian, Yuxian Meng +4
In this paper, we propose a new paradigm for paraphrase generation by treating the task as unsupervised machine translation (UMT) based on the assumption that there must be pairs o…
ConRPG: Paraphrase Generation using Contexts as Regularizer
Yuxian Meng, Xiang Ao, Qing He +5
A long-standing issue with paraphrase generation is how to obtain reliable supervision signals. In this paper, we propose an unsupervised paradigm for paraphrase generation based o…
Layer-wise Model Pruning based on Mutual Information
Chun Fan, Jiwei Li, Xiang Ao +3
The proposed pruning strategy offers merits over weight-based pruning techniques: (1) it avoids irregular memory access since representations and matrices can be squeezed into thei…
Folden: -Fold Ensemble for Out-Of-Distribution Detection
Xiaoya Li, Jiwei Li, Xiaofei Sun +5
Out-of-Distribution (OOD) detection is an important problem in natural language processing (NLP). In this work, we propose a simple yet effective framework Folden, which mimics…
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
Zijun Sun, Xiaoya Li, Xiaofei Sun +6
Recent pretraining models in Chinese neglect two important aspects specific to the Chinese language: glyph and pinyin, which carry significant syntax and semantic information for l…
Defending Against Backdoor Attacks in Natural Language Generation
Xiaofei Sun, Xiaoya Li, Yuxian Meng +4
The frustratingly fragile nature of neural network models make current natural language generation (NLG) systems prone to backdoor attacks and generate malicious sequences that cou…
Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation
Shuhe Wang, Yuxian Meng, Xiaofei Sun +5
Multi-modal dialog modeling is of growing interest. In this work, we propose frameworks to resolve a specific case of multi-modal dialog generation that better mimics multi-modal d…
Fast Nearest Neighbor Machine Translation
Yuxian Meng, Xiaoya Li, Xiayu Zheng +4
Though nearest neighbor Machine Translation (NN-MT) \citep{khandelwal2020nearest} has proved to introduce significant performance boosts over standard neural MT systems, it is p…
Dependency Parsing as MRC-based Span-Span Prediction
Leilei Gan, Yuxian Meng, Kun Kuang +4
Higher-order methods for dependency parsing can partially but not fully address the issue that edges in dependency trees should be constructed at the text span/subtree level rather…
Sentence Similarity Based on Contexts
Xiaofei Sun, Yuxian Meng, Xiang Ao +4
Existing methods to measure sentence similarity are faced with two challenges: (1) labeled datasets are usually limited in size, making them insufficient to train supervised neural…
BertGCN: Transductive Text Classification by Combining GCN and BERT
Yuxiao Lin, Yuxian Meng, Xiaofei Sun +4
In this work, we propose BertGCN, a model that combines large scale pretraining and transductive learning for text classification. BertGCN constructs a heterogeneous graph over the…
Self-Explaining Structures Improve NLP Models
Zijun Sun, Chun Fan, Qinghong Han +4
Existing approaches to explaining deep learning models in NLP usually suffer from two major drawbacks: (1) the main model and the explaining model are decoupled: an additional prob…
OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts
Yuxian Meng, Shuhe Wang, Qinghong Han +4
When humans converse, what a speaker will say next significantly depends on what he sees. Unfortunately, existing dialogue models generate dialogue utterances only based on precedi…
Neural Semi-supervised Learning for Text Classification Under Large-Scale Pretraining
Zijun Sun, Chun Fan, Xiaofei Sun +3
The goal of semi-supervised learning is to utilize the unlabeled, in-domain dataset U to improve models trained on the labeled dataset D. Under the context of large-scale language-…
Pair the Dots: Jointly Examining Training History and Test Stimuli for Model Interpretability
Yuxian Meng, Chun Fan, Zijun Sun +3
Any prediction from a model is made by a combination of learning history and test stimuli. This provides significant insights for improving model interpretability: {\it because of…