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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16901–16950 of 17610 papers

TitleStatusHype
Sentiment Analysis on Monolingual, Multilingual and Code-Switching Twitter Corpora—0
Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation—0
Learning Word Meanings and Grammar for Describing Everyday Activities in Smart Environments—0
Reverse-engineering Language: A Study on the Semantic Compositionality of German Compounds—0
ParFDA for Fast Deployment of Accurate Statistical Machine Translation Systems, Benchmarks, and Statistics—0
Natural Language Generation from Pictographs—0
Touch-Based Pre-Post-Editing of Machine Translation Output—0
Open-Domain Name Error Detection using a Multi-Task RNN—0
The RWTH Aachen German-English Machine Translation System for WMT 2015—0
Results of the WMT15 Tuning Shared Task—0
Transducer Disambiguation with Sparse Topological Features—0
SHEF-NN: Translation Quality Estimation with Neural Networks—0
Reinforcing the Topic of Embeddings with Theta Pure Dependence for Text Classification—0
Statistical Machine Translation Improvement based on Phrase Selection—0
Predicting Pronouns across Languages with Continuous Word Spaces—0
Script Induction as Language Modeling—0
Predicting Machine Translation Adequacy with Document Embeddings—0
Statistical Machine Translation with Automatic Identification of Translationese—0
The Karlsruhe Institute of Technology Translation Systems for the WMT 2015—0
Auto-Sizing Neural Networks: With Applications to n-gram Language Models—0
End-to-End Attention-based Large Vocabulary Speech RecognitionCode0
Probabilistic Modelling of Morphologically Rich Languages—0
Online Representation Learning in Recurrent Neural Language Models—0
Depth-Gated LSTM—0
Deep Graph Kernels—0
Finding Function in Form: Compositional Character Models for Open Vocabulary Word RepresentationCode0
An End-to-End Neural Network for Polyphonic Piano Music TranscriptionCode0
Sublinear Partition EstimationCode0
Handwritten Text Recognition Results on the Bentham Collection with Improved Classical N-Gram-HMM methods—0
Classifying Syntactic Categories in the Chinese Dependency Network—0
Discriminative Segmental Cascades for Feature-Rich Phone Recognition—0
Grid Long Short-Term MemoryCode0
Dependency Recurrent Neural Language Models for Sentence CompletionCode0
genCNN: A Convolutional Architecture for Word Sequence Prediction—0
Bilingual Word Embeddings from Non-Parallel Document-Aligned Data Applied to Bilingual Lexicon Induction—0
Analyzing Optimization for Statistical Machine Translation: MERT Learns Verbosity, PRO Learns Length—0
Chinese Grammatical Error Diagnosis System Based on Hybrid Model—0
Deep Markov Neural Network for Sequential Data Classification—0
Incremental Recurrent Neural Network Dependency Parser with Search-based Discriminative Training—0
Evaluating distributed word representations for capturing semantics of biomedical concepts—0
DCU-ADAPT: Learning Edit Operations for Microblog Normalisation with the Generalised Perceptron—0
Chinese Spelling Check System Based on N-gram Model—0
Inducing Word and Part-of-Speech with Pitman-Yor Hidden Semi-Markov Models—0
Arib@QALB-2015 Shared Task: A Hybrid Cascade Model for Arabic Spelling Error Detection and Correction—0
Improving Pivot Translation by Remembering the Pivot—0
Entity Retrieval via Entity Factoid Hierarchy—0
Dependency Parsing with Graph Rewriting—0
Generative Incremental Dependency Parsing with Neural Networks—0
Instance Selection Improves Cross-Lingual Model Training for Fine-Grained Sentiment Analysis—0
GWU-HASP-2015@QALB-2015 Shared Task: Priming Spelling Candidates with Probability—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