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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16351–16400 of 17610 papers

TitleStatusHype
Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities—0
Self-organized Hierarchical Softmax—0
Synthesising Sign Language from semantics, approaching "from the target and back"—0
Transition-Based Generation from Abstract Meaning RepresentationsCode0
LV-ROVER: Lexicon Verified Recognizer Output Voting Error Reduction—0
Exploring Neural Transducers for End-to-End Speech Recognition—0
Language modeling with Neural trans-dimensional random fields—0
OBJ2TEXT: Generating Visually Descriptive Language from Object Layouts—0
Attention-Based End-to-End Speech Recognition on Voice Search—0
High-risk learning: acquiring new word vectors from tiny dataCode0
Syllable-aware Neural Language Models: A Failure to Beat Character-aware OnesCode0
Learning Visually Grounded Sentence Representations—0
Improving Language Modeling using Densely Connected Recurrent Neural Networks—0
On the State of the Art of Evaluation in Neural Language ModelsCode0
A Simple Language Model based on PMI Matrix Approximations—0
Do Neural Nets Learn Statistical Laws behind Natural Language?—0
Controlling Linguistic Style Aspects in Neural Language Generation—0
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network—0
An Embedded Deep Learning based Word PredictionCode0
Multiscale sequence modeling with a learned dictionary—0
SAM: Semantic Attribute Modulation for Language Modeling and Style Variation—0
Joint CTC/attention decoding for end-to-end speech recognition—0
Sentence Embedding for Neural Machine Translation Domain Adaptation—0
ER3: A Unified Framework for Event Retrieval, Recognition and Recounting—0
Improved Word Representation Learning with SememesCode0
Detecting annotation noise in automatically labelled data—0
Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)—0
Automatically Generating Rhythmic Verse with Neural Networks—0
Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding—0
Alignment at Work: Using Language to Distinguish the Internalization and Self-Regulation Components of Cultural Fit in Organizations—0
Weighted-Entropy-Based Quantization for Deep Neural Networks—0
Generative Bridging Network in Neural Sequence Prediction—0
Comparison of Modified Kneser-Ney and Witten-Bell Smoothing Techniques in Statistical Language Model of Bahasa Indonesia—0
Device Placement Optimization with Reinforcement LearningCode0
Exploring the Syntactic Abilities of RNNs with Multi-task LearningCode0
YellowFin and the Art of Momentum TuningCode0
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LMCode0
Gated Recurrent Neural Tensor Network—0
Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning—0
Visual attention models for scene text recognition—0
Learning to Compute Word Embeddings On the Fly—0
當代非監督式方法之比較於節錄式語音摘要 (An Empirical Comparison of Contemporary Unsupervised Approaches for Extractive Speech Summarization) [In Chinese]—0
Biased Importance Sampling for Deep Neural Network TrainingCode0
Adversarial Generation of Natural Language—0
Accelerating Neural Architecture Search using Performance PredictionCode0
Who's to say what's funny? A computer using Language Models and Deep Learning, That's Who!—0
Semi-Supervised Model Training for Unbounded Conversational Speech Recognition—0
Deriving Neural Architectures from Sequence and Graph Kernels—0
Fast-Slow Recurrent Neural NetworksCode0
Unbiasing Truncated Backpropagation Through Time—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