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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15401–15450 of 17610 papers

TitleStatusHype
Cross-Domain NER using Cross-Domain Language ModelingCode0
Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition—0
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction—0
Large Dataset and Language Model Fun-Tuning for Humor Recognition—0
Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain AdaptationCode0
Online Infix Probability Computation for Probabilistic Finite Automata—0
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling—0
Language Modeling with Shared Grammar—0
Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation—0
Training Hybrid Language Models by Marginalizing over Segmentations—0
Context-specific Language Modeling for Human Trafficking Detection from Online Advertisements—0
Stochastic Tokenization with a Language Model for Neural Text Classification—0
Latent Structure Models for Natural Language Processing—0
Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR—0
Evaluating Language Model Finetuning Techniques for Low-resource LanguagesCode1
Multiplicative Models for Recurrent Language Modeling—0
GPT-based Generation for Classical Chinese PoetryCode0
Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing TasksCode0
Inducing Syntactic Trees from BERT Representations—0
EmotionX-KU: BERT-Max based Contextual Emotion ClassifierCode0
A Tensorized Transformer for Language ModelingCode1
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense DisambiguationCode1
Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRFCode0
Semi-supervised acoustic model training for five-lingual code-switched ASR—0
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation ExtractionCode0
XLNet: Generalized Autoregressive Pretraining for Language UnderstandingCode1
Pre-Training with Whole Word Masking for Chinese BERTCode3
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model—0
Multi-Graph Decoding for Code-Switching ASR—0
Towards Robust Named Entity Recognition for Historic GermanCode0
Structured Pruning of Recurrent Neural Networks through Neuron Selection—0
Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language ModelingCode0
Dispersed Exponential Family Mixture VAEs for Interpretable Text GenerationCode0
One Epoch Is All You Need—0
Automatic Conditional Generation of Personalized Social Media Short Texts—0
Scalable Syntax-Aware Language Models Using Knowledge Distillation—0
On the Effect of Word Order on Cross-lingual Sentiment Analysis—0
Telephonetic: Making Neural Language Models Robust to ASR and Semantic Noise—0
Character n-gram Embeddings to Improve RNN Language Models—0
A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics—0
UCAM Biomedical translation at WMT19: Transfer learning multi-domain ensembles—0
Putting words in context: LSTM language models and lexical ambiguityCode0
Continual and Multi-Task Architecture SearchCode0
Cued@wmt19:ewc&lms—0
Federated Learning for Emoji Prediction in a Mobile Keyboard—0
What Kind of Language Is Hard to Language-Model?—0
What Does BERT Look At? An Analysis of BERT's AttentionCode0
Improving Neural Language Modeling via Adversarial TrainingCode0
A Survey on Neural Network Language Models—0
Analyzing the Structure of Attention in a Transformer Language Model—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