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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15451–15500 of 17610 papers

TitleStatusHype
Neural Shuffle-Exchange Networks -- Sequence Processing in O(n log n) TimeCode0
Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor DetectionCode0
Investigation on N-gram Approximated RNNLMs for Recognition of Morphologically Rich Speech—0
Agglomerative AttentionCode0
Learn Spelling from Teachers: Transferring Knowledge from Language Models to Sequence-to-Sequence Speech Recognition—0
The University of Edinburgh's Submissions to the WMT19 News Translation Task—0
R-Transformer: Recurrent Neural Network Enhanced TransformerCode0
Large Memory Layers with Product KeysCode0
Can Unconditional Language Models Recover Arbitrary Sentences?—0
Improving the Performance of the LSTM and HMM Model via Hybridization—0
Exploring Conditioning for Generative Music Systems with Human-Interpretable Controls—0
Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech RecognitionCode0
UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference—0
Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile—0
To Tune or Not To Tune? How About the Best of Both Worlds?Code0
Listen, Attend, Spell and Adapt: Speaker Adapted Sequence-to-Sequence ASR—0
Applying a Pre-trained Language Model to Spanish Twitter Humor PredictionCode0
Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training—0
Augmenting Self-attention with Persistent MemoryCode0
Kite: Automatic speech recognition for unmanned aerial vehicles—0
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction—0
Large Dataset and Language Model Fun-Tuning for Humor Recognition—0
Language Modeling with Shared Grammar—0
Latent Structure Models for Natural Language Processing—0
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling—0
Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain AdaptationCode0
Online Infix Probability Computation for Probabilistic Finite Automata—0
Training Hybrid Language Models by Marginalizing over Segmentations—0
Stochastic Tokenization with a Language Model for Neural Text Classification—0
Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge—0
Cross-Domain NER using Cross-Domain Language ModelingCode0
Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR—0
Barack's Wife Hillary: Using Knowledge Graphs for Fact-Aware Language ModelingCode0
Interpolated Spectral NGram Language Models—0
Estimating senses with sets of lexically related words for Polish word sense disambiguation—0
Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition—0
Context-specific Language Modeling for Human Trafficking Detection from Online Advertisements—0
Flamb\'e: A Customizable Framework for Machine Learning Experiments—0
A Surprisingly Robust Trick for the Winograd Schema Challenge—0
Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation—0
Multiplicative Models for Recurrent Language Modeling—0
GPT-based Generation for Classical Chinese PoetryCode0
Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing TasksCode0
EmotionX-KU: BERT-Max based Contextual Emotion ClassifierCode0
Inducing Syntactic Trees from BERT Representations—0
Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRFCode0
Semi-supervised acoustic model training for five-lingual code-switched ASR—0
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model—0
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation ExtractionCode0
Multi-Graph Decoding for Code-Switching ASR—0
Show:102550
← PrevPage 310 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