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Methodology
← Resources & Methods
Natural Language Processing
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Resources & Methods
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Language Modeling
1090 directly classified papers
Papers per year
2003: 2
2006: 3
2007: 5
2008: 2
2009: 2
2011: 1
2012: 3
2013: 4
2014: 2
2015: 1
2016: 10
2017: 25
2018: 33
2019: 52
2020: 93
2021: 125
2022: 232
2023: 156
2024: 139
2025: 199
2026: 1
Papers
Frequency Effects on Syntactic Rule Learning in Transformers
EMNLP 2021
You should evaluate your language model on marginal likelihood over tokenisations
EMNLP 2021
Chinese WPLC: A Chinese Dataset for Evaluating Pretrained Language Models on Word Prediction Given Long-Range Context
EMNLP 2021
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models
EMNLP 2021
Improving and Simplifying Pattern Exploiting Training
EMNLP 2021
Distilling Relation Embeddings from Pretrained Language Models
EMNLP 2021
WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach
EMNLP 2021
Probing Across Time: What Does RoBERTa Know and When?
EMNLP 2021
Learning and Evaluating a Differentially Private Pre-trained Language Model
EMNLP 2021
How Does Fine-tuning Affect the Geometry of Embedding Space: A Case Study on Isotropy
EMNLP 2021
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models
EMNLP 2021
The Low-Dimensional Linear Geometry of Contextualized Word Representations
EMNLP 2021
NOPE: A Corpus of Naturally-Occurring Presuppositions in English
EMNLP 2021
Modeling the Interaction Between Perception-Based and Production-Based Learning in Children’s Early Acquisition of Semantic Knowledge
EMNLP 2021
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
ACL 2021
Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models
EMNLP 2021
Mitigating Language-Dependent Ethnic Bias in BERT
EMNLP 2021
The Impact of Positional Encodings on Multilingual Compression
EMNLP 2021
Do Long-Range Language Models Actually Use Long-Range Context?
EMNLP 2021
Condenser: a Pre-training Architecture for Dense Retrieval
EMNLP 2021
Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you?
EMNLP 2021
How much pretraining data do language models need to learn syntax?
EMNLP 2021
Competency Problems: On Finding and Removing Artifacts in Language Data
EMNLP 2021
Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes
EMNLP 2021
Linguistic Dependencies and Statistical Dependence
EMNLP 2021
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