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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15701–15750 of 17610 papers

TitleStatusHype
Practical Text Classification With Large Pre-Trained Language ModelsCode0
Quantification and Analysis of Scientific Language Variation Across Research Fields—0
Parameter Re-Initialization through Cyclical Batch Size Schedules—0
Comparing Neural- and N-Gram-Based Language Models for Word Segmentation—0
Effectiveness of Character Language Model for Vietnamese Named Entity Recognition—0
Universal Language Model Fine-tuning for Patent Classification—0
Incorporating Context into Language Encoding Models for fMRI—0
A Simple Cache Model for Image Recognition—0
Deep Multimodal Learning: An Effective Method for Video Classification—0
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent—0
Acoustics-guided evaluation (AGE): a new measure for estimating performance of speech enhancement algorithms for robust ASR—0
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural NetworkCode0
Multi-level Multimodal Common Semantic Space for Image-Phrase GroundingCode0
Are 2D-LSTM really dead for offline text recognition?—0
Learning to Discover, Ground and Use Words with Segmental Neural Language Models—0
Alignment Analysis of Sequential Segmentation of Lexicons to Improve Automatic Cognate DetectionCode0
Quantifying Uncertainties in Natural Language Processing Tasks—0
Multi-cell LSTM Based Neural Language Model—0
Extractive Summary as Discrete Latent Variables—0
Exploring RNN-Transducer for Chinese Speech Recognition—0
An Online Attention-based Model for Speech Recognition—0
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent AgentsCode0
Modeling Local Dependence in Natural Language with Multi-channel Recurrent Neural Networks—0
Modular Networks: Learning to Decompose Neural Computation—0
Forecasting People's Needs in Hurricane Events from Social Network—0
Fine-tuning of Language Models with Discriminator—0
Federated Learning for Mobile Keyboard PredictionCode0
Effective Representation for Easy-First Dependency ParsingCode0
Compositional Language Understanding with Text-based Relational ReasoningCode0
Language model integration based on memory control for sequence to sequence speech recognition—0
Discriminative training of RNNLMs with the average word error criterion—0
Transfer learning of language-independent end-to-end ASR with language model fusion—0
The Marchex 2018 English Conversational Telephone Speech Recognition System—0
Mesh-TensorFlow: Deep Learning for Supercomputers—0
Do RNNs learn human-like abstract word order preferences?Code0
Towards Unsupervised Speech-to-Text Translation—0
Progress and Tradeoffs in Neural Language Models—0
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data TasksCode0
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation—0
Analysing Dropout and Compounding Errors in Neural Language Models—0
Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Model—0
Cycle-consistency training for end-to-end speech recognition—0
Does Syntactic Knowledge in Multilingual Language Models Transfer Across Languages?—0
Closing Brackets with Recurrent Neural Networks—0
Convolutions Are All You Need (For Classifying Character Sequences)—0
Importance of Self-Attention for Sentiment Analysis—0
`Indicatements' that character language models learn English morpho-syntactic units and regularities—0
Er ... well, it matters, right? On the role of data representations in spoken language dependency parsing—0
Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models—0
Learning to Define Terms in the Software Domain—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