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Found 36 papers, 32 papers with code
Going “Deeper”: Structured Sememe Prediction via Transformer with Tree Attention
However, all existing sememe prediction studies ignore the hierarchical structures of sememes, which are important in the sememe-based semantic description system.
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained Models
To validate our viewpoints, we design two methods to evaluate the robustness of FMS: (1) model disguise attack, which post-trains an inferior PTM with a contrastive objective, and (2) evaluation data selection, which selects a subset of the data points for FMS evaluation based on K-means clustering.
WebCPM: Interactive Web Search for Chinese Long-form Question Answering
We recruit annotators to search for relevant information using our interface and then answer questions.
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP
We discuss the deficiencies in previous work and propose our suggestions that the research on the Security-oriented adversarial NLP (SoadNLP) should: (1) evaluate their methods on security tasks to demonstrate the real-world concerns; (2) consider real-world attackers' goals, instead of developing impractical methods.
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information
In this paper, we utilize the multilingual synonyms, multilingual glosses and images in BabelNet for SPBS.
QuoteR: A Benchmark of Quote Recommendation for Writing
To facilitate the research on this task, we build a large and fully open quote recommendation dataset called QuoteR, which comprises three parts including English, standard Chinese and classical Chinese.
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic.
Textual Backdoor Attacks Can Be More Harmful via Two Simple Tricks
In this paper, we find two simple tricks that can make existing textual backdoor attacks much more harmful.
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer
In this paper, we make the first attempt to conduct adversarial and backdoor attacks based on text style transfer, which is aimed at altering the style of a sentence while preserving its meaning.
CPM-2: Large-scale Cost-effective Pre-trained Language Models
We present a suite of cost-effective techniques for the use of PLMs to deal with the efficiency issues of pre-training, fine-tuning, and inference.
Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution
Recent studies show that neural natural language processing (NLP) models are vulnerable to backdoor attacks.
CCPM: A Chinese Classical Poetry Matching Dataset
We hope this dataset can further enhance the study on incorporating deep semantics into the understanding and generation system of Chinese classical poetry.
Sub-Character Tokenization for Chinese Pretrained Language Models
2) Pronunciation-based SubChar tokenizers can encode Chinese homophones into the same transliteration sequences and produce the same tokenization output, hence being robust to homophone typos.
Automatic Construction of Sememe Knowledge Bases via Dictionaries
We use this method to build an English SKB and a French SKB, and conduct comprehensive evaluations from both intrinsic and extrinsic perspectives.
Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger
As far as we know, almost all existing textual backdoor attack methods insert additional contents into normal samples as triggers, which causes the trigger-embedded samples to be detected and the backdoor attacks to be blocked without much effort.
Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-Level Backdoor Attacks
In this work, we demonstrate the universal vulnerability of PTMs, where fine-tuned PTMs can be easily controlled by backdoor attacks in arbitrary downstream tasks.
Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning
In this work, we propose a simple and effective method to cover a much larger proportion of the attack search space, called Adversarial and Mixup Data Augmentation (AMDA).
CPM: A Large-scale Generative Chinese Pre-trained Language Model
However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available.
Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet
In this paper, we propose a new unsupervised method for HowNet-based Chinese WSD, which exploits the masked language model task of pre-trained language models.
ONION: A Simple and Effective Defense Against Textual Backdoor Attacks
Nevertheless, there are few studies on defending against textual backdoor attacks.
Know What You Don't Need: Single-Shot Meta-Pruning for Attention Heads
To measure the informativeness of attention heads, we train our Single-Shot Meta-Pruner (SMP) with a meta-learning paradigm aiming to maintain the distribution of text representations after pruning.
WantWords: An Open-source Online Reverse Dictionary System
A reverse dictionary takes descriptions of words as input and outputs words semantically matching the input descriptions.
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations
Adversarial attacking aims to fool deep neural networks with adversarial examples.
OpenAttack: An Open-source Textual Adversarial Attack Toolkit
Textual adversarial attacking has received wide and increasing attention in recent years.
Country Image in COVID-19 Pandemic: A Case Study of China
Therefore, in this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Lexical Sememe Prediction using Dictionary Definitions by Capturing Local Semantic Correspondence
We find that sememes of each word are usually semantically matched to different words in its dictionary definition, and we name this matching relationship local semantic correspondence.
Multi-channel Reverse Dictionary Model
A reverse dictionary takes the description of a target word as input and outputs the target word together with other words that match the description.
Towards Building a Multilingual Sememe Knowledge Base: Predicting Sememes for BabelNet Synsets
We first build a dataset serving as the seed of the multilingual sememe KB.
Word-level Textual Adversarial Attacking as Combinatorial Optimization
Also, further experiments show our model has higher transferability and can bring more robustness enhancement to victim models by adversarial training.
Improving Sequence Modeling Ability of Recurrent Neural Networks via Sememes
Sememes, the minimum semantic units of human languages, have been successfully utilized in various natural language processing applications.
Using BERT for Word Sense Disambiguation
Word Sense Disambiguation (WSD), which aims to identify the correct sense of a given polyseme, is a long-standing problem in NLP.
Modeling Semantic Compositionality with Sememe Knowledge
In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment.
multi-word expression embedding
multi-word expression sememe prediction
COS960: A Chinese Word Similarity Dataset of 960 Word Pairs
Word similarity computation is a widely recognized task in the field of lexical semantics.
OpenHowNet: An Open Sememe-based Lexical Knowledge Base
In this paper, we present an open sememe-based lexical knowledge base OpenHowNet.
Cross-lingual Lexical Sememe Prediction
We propose a novel framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction.
