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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15901–15950 of 17610 papers

TitleStatusHype
What represents ``style'' in authorship attribution?—0
Transfer Learning for a Letter-Ngrams to Word Decoder in the Context of Historical Handwriting Recognition with Scarce Resources—0
Reproducing and Regularizing the SCRN ModelCode0
RNN Simulations of Grammaticality Judgments on Long-distance DependenciesCode0
Neural Machine Translation with Decoding History Enhanced Attention—0
A Deep Dive into Word Sense Disambiguation with LSTM—0
Contextual String Embeddings for Sequence LabelingCode0
Birzeit Arabic Dialect Identification System for the 2018 VarDial Challenge—0
Addressing the Winograd Schema Challenge as a Sequence Ranking Task—0
Kawenn\'on:nis: the Wordmaker for Kanyen'k\'eha—0
Iterative Language Model Adaptation for Indo-Aryan Language Identification—0
Code-Switching Detection with Data-Augmented Acoustic and Language Models—0
Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech—0
A Hierarchical Approach to Neural Context-Aware Modeling—0
A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech RecognitionCode0
"Bilingual Expert" Can Find Translation ErrorsCode0
Automatic Speech Recognition for Humanitarian Applications in Somali—0
Acoustic-to-Word Recognition with Sequence-to-Sequence Models—0
What is not where: the challenge of integrating spatial representations into deep learning architectures—0
Hierarchical Multi Task Learning With CTC—0
Improving Explainable Recommendations with Synthetic Reviews—0
Guess who? Multilingual approach for the automated generation of author-stylized poetry—0
Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units—0
A Comparison of Adaptation Techniques and Recurrent Neural Network ArchitecturesCode0
Iterative evaluation of LSTM cells—0
Universal TransformersCode0
Multi-D Kneser-Ney Smoothing Preserving the Original Marginal Distributions—0
Revisiting the Hierarchical Multiscale LSTM—0
Deep-speare: A Joint Neural Model of Poetic Language, Meter and RhymeCode0
Video Captioning with Boundary-aware Hierarchical Language Decoding and Joint Video Prediction—0
Learning The Sequential Temporal Information with Recurrent Neural Networks—0
Improved training of neural trans-dimensional random field language models with dynamic noise-contrastive estimationCode0
Neural Random Projections for Language Modelling—0
A Unified Neural Architecture for Joint Dialog Act Segmentation and Recognition in Spoken Dialog System—0
Feature Optimization for Predicting Readability of Arabic L1 and L2—0
A Hybrid Learning Scheme for Chinese Word Embedding—0
CYUT-III Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2018 CGED Shared Task—0
Compositional Language Modeling for Icon-Based Augmentative and Alternative Communication—0
Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLPCode0
Enhancement of Encoder and Attention Using Target Monolingual Corpora in Neural Machine Translation—0
Language Informed Modeling of Code-Switched Text—0
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences—0
Text Completion using Context-Integrated Dependency Parsing—0
On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data—0
Thank ``Goodness''! A Way to Measure Style in Student Essays—0
Language Production Dynamics with Recurrent Neural Networks—0
NILC-SWORNEMO at the Surface Realization Shared Task: Exploring Syntax-Based Word Ordering using Neural Models—0
Joint Part-of-Speech and Language ID Tagging for Code-Switched Data—0
The OSU Realizer for SRST `18: Neural Sequence-to-Sequence Inflection and Incremental Locality-Based Linearization—0
Investigating Effective Parameters for Fine-tuning of Word Embeddings Using Only a Small Corpus—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