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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16451–16500 of 17610 papers

TitleStatusHype
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
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
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
Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network—0
End-to-End ASR-free Keyword Search from Speech—0
Context-aware Captions from Context-agnostic SupervisionCode0
Unsupervised neural and Bayesian models for zero-resource speech processing—0
Graph Databases for Designing High-Performance Speech Recognition Grammars—0
Head-Lexicalized Bidirectional Tree LSTMs—0
Nonparametric Bayesian Semi-supervised Word Segmentation—0
Learning Visual N-Grams from Web Data—0
Structured Sequence Modeling with Graph Convolutional Recurrent NetworksCode0
Low-dimensional Query Projection based on Divergence Minimization Feedback Model for Ad-hoc Retrieval—0
Continuous multilinguality with language vectors—0
An Empirical Study of Language CNN for Image CaptioningCode0
A recurrent neural network without chaos—0
Neural networks based EEG-Speech Models—0
Improving Neural Language Models with a Continuous CacheCode0
Context-aware Sentiment Word Identification: sentiword2vec—0
A Character-Word Compositional Neural Language Model for FinnishCode0
Towards better decoding and language model integration in sequence to sequence models—0
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image CaptioningCode0
Areas of Attention for Image Captioning—0
Bayesian Language Model based on Mixture of Segmental Contexts for Spontaneous Utterances with Unexpected Words—0
Integrating Optical Character Recognition and Machine Translation of Historical Documents—0
Classifying ASR Transcriptions According to Arabic Dialect—0
Dependency grammars as Haskell programs—0
A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation—0
DSL Shared Task 2016: Perfect Is The Enemy of Good Language Discrimination Through Expectation--Maximization and Chunk-based Language Model—0
ACE: Automatic Colloquialism, Typographical and Orthographic Errors Detection for Chinese Language—0
Fast Gated Neural Domain Adaptation: Language Model as a Case Study—0
Improved Word Embeddings with Implicit Structure Information—0
Fast Collocation-Based Bayesian HMM Word Alignment—0
Extracting Social Networks from Literary Text with Word Embedding Tools—0
Automated speech-unit delimitation in spoken learner English—0
How Many Languages Can a Language Model Model?—0
IITP English-Hindi Machine Translation System at WAT 2016—0
Chinese Preposition Selection for Grammatical Error Diagnosis—0
IIT Bombay's English-Indonesian submission at WAT: Integrating Neural Language Models with SMT—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