SOTAVerified

Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

Showing 1435114400 of 17610 papers

TitleStatusHype
Exploring Looping Effects in RNN-based Architectures0
BertAA : BERT fine-tuning for Authorship Attribution0
Homonym normalisation by word sense clustering: a case in Japanese0
Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network0
CLPLM: Character Level Pretrained Language Model for ExtractingSupport Phrases for Sentiment Labels0
Automatic Assistance for Academic Word Usage0
Amobee at SemEval-2020 Task 7: Regularization of Language Model Based Classifiers0
Incremental Neural Lexical Coherence ModelingCode0
Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification0
Contextual Augmentation of Pretrained Language Models for Emotion Recognition in Conversations0
A Deep Generative Approach to Native Language Identification0
Arabizi Language Models for Sentiment Analysis0
Unsupervised Aspect-Level Sentiment Controllable Style Transfer0
YNU-HPCC at SemEval-2020 Task 10: Using a Multi-granularity Ordinal Classification of the BiLSTM Model for Emphasis Selection0
YNUtaoxin at SemEval-2020 Task 11: Identification Fragments of Propaganda Technique by Neural Sequence Labeling Models with Different Tagging Schemes and Pre-trained Language Model0
Unifying Input and Output Smoothing in Neural Machine Translation0
XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages0
UnihanLM: Coarse-to-Fine Chinese-Japanese Language Model Pretraining with the Unihan DatabaseCode0
Language Model Transformers as Evaluators for Open-domain DialoguesCode0
Surface Realization Using Pretrained Language Models0
Smash at SemEval-2020 Task 7: Optimizing the Hyperparameters of ERNIE 2.0 for Humor Ranking and Rating0
Multi-Word Lexical SimplificationCode0
UI at SemEval-2020 Task 8: Text-Image Fusion for Sentiment Classification0
MultiVitaminBooster at PARSEME Shared Task 2020: Combining Window- and Dependency-Based Features with Multilingual Contextualised Word Embeddings for VMWE Detection0
MineriaUNAM at SemEval-2020 Task 3: Predicting Contextual WordSimilarity Using a Centroid Based Approach and Word Embeddings0
TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation0
SWAGex at SemEval-2020 Task 4: Commonsense Explanation as Next Event Prediction0
Training Linear Finite-State Machines0
Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models0
Merge and Recognize: A Geometry and 2D Context Aware Graph Model for Named Entity Recognition from Visual Documents0
ProsperAMnet at FinCausal 2020, Task 1 & 2: Modeling causality in financial texts using multi-headed transformers0
Mxgra at SemEval-2020 Task 4: Common Sense Making with Next Token Prediction0
JUST at SemEval-2020 Task 11: Detecting Propaganda Techniques Using BERT Pre-trained Model0
TextLearner at SemEval-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text0
Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions0
Towards Generating Query to Perform Query Focused Abstractive Summarization using Pre-trained ModelCode0
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification0
Monolingual and Multilingual Reduction of Gender Bias in Contextualized RepresentationsCode0
LT3 at SemEval-2020 Task 7: Comparing Feature-Based and Transformer-Based Approaches to Detect Funny Headlines0
Speech Disfluencies occur at Higher Perplexities0
Neural Language Modeling for Named Entity Recognition0
Self-Supervised Relationship Probing0
Masked Reasoner at SemEval-2020 Task 4: Fine-Tuning RoBERTa for Commonsense Reasoning0
Neural language models for text classification in evidence-based medicine0
Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations0
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion0
Coarse-to-Fine Memory Matching for Joint Retrieval and ClassificationCode0
Disentangling Homophemes in Lip Reading using Perplexity Analysis0
Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-190
An Investigation of Language Model Interpretability via Sentence EditingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified