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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep LearningCode0
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context ModelingCode0
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified TextCode0
Nested LSTMsCode0
Accelerating recurrent neural network language model based online speech recognition system—0
Discrete Autoencoders for Sequence ModelsCode0
A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error CorrectionCode0
Enhancing Translation Language Models with Word Embedding for Information Retrieval—0
Stochastic Learning of Nonstationary Kernels for Natural Language Modeling—0
Exploring Architectures, Data and Units For Streaming End-to-End Speech Recognition with RNN-Transducer—0
From Information Bottleneck To Activation Norm Penalty—0
EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS—0
Convolutional Sequence Modeling Revisited—0
A Simple Fully Connected Network for Composing Word Embeddings from Characters—0
Gated ConvNets for Letter-Based ASR—0
Distributed Fine-tuning of Language Models on Private Data—0
Dense Recurrent Neural Network with Attention Gate—0
A Goal-oriented Neural Conversation Model by Self-Play—0
Realtime query completion via deep language models—0
Learning Document Embeddings With CNNs—0
Predictive power of word surprisal for reading times is a linear function of language model quality—0
LSH Softmax: Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures—0
Noise-Based Regularizers for Recurrent Neural Networks—0
Revisiting Bayes by Backprop—0
Learning to Write by Learning the Objective—0
Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction—0
LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES—0
New Baseline in Automatic Speech Recognition for Northern S\'ami—0
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
Improving Generalization Performance by Switching from Adam to SGDCode0
Differentially Private Distributed Learning for Language Modeling Tasks—0
A Flexible Approach to Automated RNN Architecture Generation—0
Subword and Crossword Units for CTC Acoustic Models—0
StrassenNets: Deep Learning with a Multiplication BudgetCode0
A Novel Way of Identifying Cyber Predators—0
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: Reducing the Communication Bandwidth for Distributed TrainingCode0
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
Phonemic Transcription of Low-Resource Tonal LanguagesCode0
SuperOCR for ALTA 2017 Shared Task—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
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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