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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16401–16450 of 17610 papers

TitleStatusHype
Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality—0
Toward Pan-Slavic NLP: Some Experiments with Language Adaptation—0
Story Cloze Task: UW NLP System—0
Query-based summarization using MDL principle—0
Spelling Correction for Morphologically Rich Language: a Case Study of Russian—0
Social Bias in Elicited Natural Language InferencesCode0
A Code-Switching Corpus of Turkish-German Conversations—0
Catching the Common Cause: Extraction and Annotation of Causal Relations and their Participants—0
Behind the Scenes of an Evolving Event Cloze Test—0
A Layered Language Model based Hybrid Approach to Automatic Full Diacritization of Arabic—0
Continuous multilinguality with language vectors—0
Derivation of Document Vectors from Adaptation of LSTM Language Model—0
Convolutional Neural Networks for Authorship Attribution of Short Texts—0
Pulling Out the Stops: Rethinking Stopword Removal for Topic Models—0
Lexicalized Reordering for Left-to-Right Hierarchical Phrase-based Translation—0
On the Need of Cross Validation for Discourse Relation Classification—0
Cross-Lingual Word Embeddings for Low-Resource Language Modeling—0
An experimental analysis of Noise-Contrastive Estimation: the noise distribution matters—0
An Extensive Empirical Evaluation of Character-Based Morphological Tagging for 14 Languages—0
URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors—0
N-gram Language Modeling using Recurrent Neural Network Estimation—0
Factorization tricks for LSTM networksCode1
Simplified End-to-End MMI Training and Voting for ASR—0
Where to put the Image in an Image Caption GeneratorCode0
Learning Simpler Language Models with the Differential State Framework—0
Simplifying the Bible and Wikipedia Using Statistical Machine Translation—0
Sequential Recurrent Neural Networks for Language Modeling—0
Direct Acoustics-to-Word Models for English Conversational Speech Recognition—0
From visual words to a visual grammar: using language modelling for image classification—0
Multichannel End-to-end Speech Recognition—0
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment ClassificationCode0
Data Noising as Smoothing in Neural Network Language ModelsCode0
English Conversational Telephone Speech Recognition by Humans and Machines—0
Evolving Deep Neural NetworksCode1
Dynamic Word EmbeddingsCode0
Frustratingly Short Attention Spans in Neural Language Modeling—0
A Hybrid Convolutional Variational Autoencoder for Text GenerationCode0
Effects of Stop Words Elimination for Arabic Information Retrieval: A Comparative Study—0
The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze TaskCode0
Bangla Word Clustering Based on Tri-gram, 4-gram and 5-gram Language Model—0
emLam -- a Hungarian Language Modeling baseline—0
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts LayerCode2
Regularizing Neural Networks by Penalizing Confident Output DistributionsCode0
First Automatic Fongbe Continuous Speech Recognition System: Development of Acoustic Models and Language ModelsCode0
Dialog Context Language Modeling with Recurrent Neural Networks—0
QCRI Machine Translation Systems for IWSLT 16—0
End-to-End ASR-free Keyword Search from Speech—0
Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network—0
Context-aware Captions from Context-agnostic SupervisionCode0
Unsupervised neural and Bayesian models for zero-resource speech processing—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