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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 16151–16200 of 17610 papers

TitleStatusHype
PronouncUR: An Urdu Pronunciation Lexicon Generator—0
Learning Continuous User Representations through Hybrid Filtering with doc2vec—0
Topic Compositional Neural Language Model—0
Letter-Based Speech Recognition with Gated ConvNetsCode0
A Flexible Approach to Automated RNN Architecture Generation—0
Improving Generalization Performance by Switching from Adam to SGDCode0
Differentially Private Distributed Learning for Language Modeling Tasks—0
Subword and Crossword Units for CTC Acoustic Models—0
A Novel Way of Identifying Cyber Predators—0
StrassenNets: Deep Learning with a Multiplication BudgetCode0
Contextualized Word Representations for Reading ComprehensionCode0
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery—0
Characterizing the hyper-parameter space of LSTM language models for mixed context applications—0
Building competitive direct acoustics-to-word models for English conversational speech recognition—0
An analysis of incorporating an external language model into a sequence-to-sequence model—0
Deep Gradient Compression Reduce the Communication Bandwidth For distributed TraningCode0
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models—0
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed TrainingCode0
State-of-the-art Speech Recognition With Sequence-to-Sequence ModelsCode1
Curriculum Design for Code-switching: Experiments with Language Identification and Language Modeling with Deep Neural Networks—0
Phonemic Transcription of Low-Resource Tonal LanguagesCode0
SuperOCR for ALTA 2017 Shared Task—0
Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading—0
Preventing Gradient Explosions in Gated Recurrent Units—0
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks—0
The power of absolute discounting: all-dimensional distribution estimation—0
Language Modeling with Recurrent Highway Hypernetworks—0
JU NITM at IJCNLP-2017 Task 5: A Classification Approach for Answer Selection in Multi-choice Question Answering System—0
N-gram Model for Chinese Grammatical Error Diagnosis—0
Chinese Spelling Check based on N-gram and String Matching Algorithm—0
Deep Learning Scaling is Predictable, Empirically—0
Text Generation Based on Generative Adversarial Nets with Latent VariableCode0
Visual Features for Context-Aware Speech Recognition—0
Slim Embedding Layers for Recurrent Neural Language Models—0
Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity RecognitionCode0
Z-Forcing: Training Stochastic Recurrent NetworksCode0
ACtuAL: Actor-Critic Under Adversarial Learning—0
RDF2Vec: RDF Graph Embeddings and Their ApplicationsCode1
Improved Twitter Sentiment Analysis Using Naive Bayes and Custom Language Model—0
Breaking the Softmax Bottleneck: A High-Rank RNN Language ModelCode0
Language Modeling for Code-Switched Data: Challenges and Approaches—0
Large-scale Cloze Test Dataset Created by Teachers—0
Block-Sparse Recurrent Neural Networks—0
Learning K-way D-dimensional Discrete Code For Compact Embedding Representations—0
Unbounded cache model for online language modeling with open vocabularyCode0
Cortical microcircuits as gated-recurrent neural networks—0
Distributed Representation for Traditional Chinese Medicine Herb via Deep Learning Models—0
Multilingual Speech Recognition With A Single End-To-End Model—0
Dual Language Models for Code Switched Speech Recognition—0
Neural Language Modeling by Jointly Learning Syntax and LexiconCode0
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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