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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 15551–15600 of 17610 papers

TitleStatusHype
SEQ\^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence CompressionCode0
MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFit—0
UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language—0
Noisy Neural Language Modeling for Typing Prediction in BCI Communication—0
nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection—0
Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples—0
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment—0
Neural GRANNy at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction—0
Rethinking Complex Neural Network Architectures for Document ClassificationCode0
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines—0
Serial Recall Effects in Neural Language Modeling—0
Multilingual prediction of Alzheimer's disease through domain adaptation and concept-based language modelling—0
Table2Vec: Neural Word and Entity Embeddings for Table Population and RetrievalCode0
A Compare-Aggregate Model with Latent Clustering for Answer Selection—0
A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension—0
Lattice-based lightly-supervised acoustic model training—0
Rethinking Full Connectivity in Recurrent Neural Networks—0
LANGUAGE MODEL EMBEDDINGS IMPROVE SENTIMENT ANALYSIS IN RUSSIANCode0
A Study of BFLOAT16 for Deep Learning Training—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
A Cost Efficient Approach to Correct OCR Errors in Large Document Collections—0
Why gradient clipping accelerates training: A theoretical justification for adaptivityCode0
Better Long-Range Dependency By Bootstrapping A Mutual Information RegularizerCode0
QuesNet: A Unified Representation for Heterogeneous Test Questions—0
Soft Contextual Data Augmentation for Neural Machine TranslationCode0
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
Dynamic Cell Structure via Recursive-Recurrent Neural Networks—0
mu-Forcing: Training Variational Recurrent Autoencoders for Text GenerationCode0
What Syntactic Structures block Dependencies in RNN Language Models?—0
An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese—0
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs—0
Deeper Text Understanding for IR with Contextual Neural Language ModelingCode0
Enhancing Domain Word Embedding via Latent Semantic ImputationCode0
Sample Efficient Text Summarization Using a Single Pre-Trained TransformerCode0
Enriching Pre-trained Language Model with Entity Information for Relation ClassificationCode0
Gmail Smart Compose: Real-Time Assisted Writing—0
Adaptively Truncating Backpropagation Through Time to Control Gradient BiasCode0
Story Ending Prediction by Transferable BERTCode0
IMHO Fine-Tuning Improves Claim DetectionCode0
Effective Sentence Scoring Method using Bidirectional Language Model for Speech Recognition—0
Joint Source-Target Self Attention with Locality ConstraintsCode0
Continual adaptation for efficient machine communication—0
Online Normalization for Training Neural NetworksCode0
Deep Residual Output Layers for Neural Language GenerationCode0
Is Word Segmentation Necessary for Deep Learning of Chinese Representations?—0
End to End Recognition System for Recognizing Offline Unconstrained Vietnamese Handwriting—0
Almost Unsupervised Text to Speech and Automatic Speech Recognition—0
Language Modeling with Deep Transformers—0
Unified Language Model Pre-training for Natural Language Understanding and GenerationCode0
A Hardware-Oriented and Memory-Efficient Method for CTC Decoding—0
Show:102550
← PrevPage 312 of 353Next →
All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified