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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16301–16350 of 17610 papers

TitleStatusHype
Modelling Word Burstiness in Natural Language: A Generalised Polya Process for Document Language Models in Information Retrieval—0
Neural Networks Compression for Language Modeling—0
CLaC @ QATS: Quality Assessment for Text Simplification—0
Syllable-level Neural Language Model for Agglutinative Language—0
Comparison of Decoding Strategies for CTC Acoustic Models—0
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic SegmentationCode0
Early Improving Recurrent Elastic Highway Network—0
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search—0
TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References—0
Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language ModelsCode0
Regularizing and Optimizing LSTM Language ModelsCode1
Revisiting Activation Regularization for Language RNNs—0
Dynamic Entity Representations in Neural Language ModelsCode0
Detecting Anxiety through RedditCode0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
Representing Compositionality based on Multiple Timescales Gated Recurrent Neural Networks with Adaptive Temporal Hierarchy for Character-Level Language Models—0
Neural Sequence-to-sequence Learning of Internal Word Structure—0
Parsing with Context Embeddings—0
Learning Word Representations with Regularization from Prior Knowledge—0
A Semi-universal Pipelined Approach to the CoNLL 2017 UD Shared Task—0
A Joint Model for Semantic Sequences: Frames, Entities, Sentiments—0
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention—0
Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.—0
UWAV at SemEval-2017 Task 7: Automated feature-based system for locating puns—0
A Generative Parser with a Discriminative Recognition Algorithm—0
Bayesian Sparsification of Recurrent Neural NetworksCode1
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
Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural NetworksCode1
Exploring Neural Transducers for End-to-End Speech Recognition—0
Transition-Based Generation from Abstract Meaning RepresentationsCode0
LV-ROVER: Lexicon Verified Recognizer Output Voting Error Reduction—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
Improving Language Modeling using Densely Connected Recurrent Neural Networks—0
Learning Visually Grounded Sentence Representations—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
Weighted-Entropy-Based Quantization for Deep Neural Networks—0
ER3: A Unified Framework for Event Retrieval, Recognition and Recounting—0
Sentence Embedding for Neural Machine Translation Domain Adaptation—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