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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16551–16600 of 17610 papers

TitleStatusHype
Learning to Capitalize with Character-Level Recurrent Neural Networks: An Empirical Study—0
Latent Tree Language ModelCode0
Parsing as Language ModelingCode0
Richer Interpolative Smoothing Based on Modified Kneser-Ney Language Modeling—0
Recurrent Neural Network Language Model Adaptation Derived Document Vector—0
Dual Learning for Machine TranslationCode0
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition—0
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks—0
Neural Machine Translation in Linear TimeCode0
Towards a continuous modeling of natural language domains—0
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes—0
Professor Forcing: A New Algorithm for Training Recurrent NetworksCode0
Broad Context Language Modeling as Reading Comprehension—0
Introduction: Cognitive Issues in Natural Language Processing—0
Achieving Human Parity in Conversational Speech Recognition—0
Translation Quality Estimation using Recurrent Neural Network—0
Compressing Neural Language Models by Sparse Word RepresentationsCode0
Generating captions without looking beyond objects—0
Language Models with Pre-Trained (GloVe) Word EmbeddingsCode0
End-to-end Concept Word Detection for Video Captioning, Retrieval, and Question Answering—0
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition—0
Challenges of Computational Processing of Code-Switching—0
Which Words Matter in Defining Phrase Reordering Behavior in Statistical Machine Translation?—0
Spoken Language Identification with Phonotactics Methods on Minangkabau, Sundanese, and Javanese Languages—0
以語言模型評估學習者文句修改前後之流暢度(Using language model to assess the fluency of learners sentences edited by teachers)[In Chinese]—0
Design of an Input Method for Taiwanese Hokkien using Unsupervized Word Segmentation for Language Modeling—0
HSSA tree structures for BTG-based preordering in machine translation—0
A Generalized Framework for Hierarchical Word Sequence Language Model—0
Philippine Language Resources: Applications, Issues, and Directions—0
Recurrent Neural Network Based Loanwords Identification in Uyghur—0
Recognizing Open-Vocabulary Relations between Objects in Images—0
Sentence Clustering using PageRank Topic Model—0
Moses \& Treex Hybrid MT Systems Bestiary—0
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks—0
HyperNetworksCode1
Pointer Sentinel Mixture ModelsCode1
Multiplicative LSTM for sequence modellingCode0
Language as a Latent Variable: Discrete Generative Models for Sentence Compression—0
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities—0
Advances in All-Neural Speech Recognition—0
The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition—0
Characterizing the Language of Online Communities and its Relation to Community Reception—0
Character-Level Language Modeling with Hierarchical Recurrent Neural Networks—0
The Microsoft 2016 Conversational Speech Recognition System—0
Purely sequence-trained neural networks for ASR based on lattice-free MMI—0
Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks—0
Hierarchical Multiscale Recurrent Neural NetworksCode0
PMI Matrix Approximations with Applications to Neural Language Modeling—0
Lexical-Morphological Modeling for Legal Text Analysis—0
Human-like Natural Language Generation Using Monte Carlo Tree Search—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