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Linguistic Acceptability
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Linguistic Acceptability is the task of determining whether a sentence is grammatical or ungrammatical.
Image Source: Warstadt et al
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Fietje: An open, efficient LLM for Dutch
Robust ASR Error Correction with Conservative Data Filtering
Learning Phonotactics from Linguistic Informants
MELA: Multilingual Evaluation of Linguistic Acceptability
Data-Free Distillation of Language Model by Text-to-Text Transfer
Not all layers are equally as important: Every Layer Counts BERT
How well can machine-generated texts be identified and can language models be trained to avoid identification?
JCoLA: Japanese Corpus of Linguistic Acceptability
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection
A Neural-Symbolic Approach Towards Identifying Grammatically Correct Sentences
NoCoLA: The Norwegian Corpus of Linguistic Acceptability
CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning
Revisiting Acceptability Judgements
Can BERT eat RuCoLA? Topological Data Analysis to Explain
ScandEval: A Benchmark for Scandinavian Natural Language Processing
ChatGPT: Jack of all trades, master of none
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE
tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation
RuCoLA: Russian Corpus of Linguistic Acceptability
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Acceptability Judgements via Examining the Topology of Attention Maps
VALUE: Understanding Dialect Disparity in NLU
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus
Revisiting the Uniform Information Density Hypothesis
General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings
An Automated Knowledge Mining and Document Classification System with Multi-model Transfer Learning
Charformer: Fast Character Transformers via Gradient-based Subword Tokenization
Using Integrated Gradients and Constituency Parse Trees to explain Linguistic Acceptability learnt by BERT
Language Models Use Monotonicity to Assess NPI Licensing
DaLAJ - a dataset for linguistic acceptability judgments for Swedish: Format, baseline, sharing
FNet: Mixing Tokens with Fourier Transforms
Entailment as Few-Shot Learner
How to Train BERT with an Academic Budget
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