SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 16651–16700 of 17610 papers

TitleStatusHype
Decomposing Bilexical Dependencies into Semantic and Syntactic Vectors—0
Shallow Discourse Parsing Using Convolutional Neural Network—0
Semi-supervised Convolutional Networks for Translation Adaptation with Tiny Amount of In-domain Data—0
Implicit Semantic Roles in a Multilingual Setting—0
Cross-lingual projection for class-based language models—0
Improving cross-domain n-gram language modelling with skipgrams—0
Exponentially Decaying Bag-of-Words Input Features for Feed-Forward Neural Network in Statistical Machine Translation—0
Character-based Neural Machine Translation—0
Chinese Couplet Generation with Neural Network Structures—0
N-gram language models for massively parallel devices—0
Normalized Log-Linear Interpolation of Backoff Language Models is Efficient—0
Larger-Context Language Modelling with Recurrent Neural Network—0
Jointly Learning to Embed and Predict with Multiple Languages—0
Modeling Concept Dependencies in a Scientific Corpus—0
Word Alignment without NULL Words—0
Unsupervised morph segmentation and statistical language models for vocabulary expansion—0
A Neural Knowledge Language Model—0
Latent Tree Language ModelCode0
Novel Word Embedding and Translation-based Language Modeling for Extractive Speech Summarization—0
Tie-breaker: Using language models to quantify gender bias in sports journalism—0
Recurrent Highway NetworksCode0
Recurrent Memory Array StructuresCode0
Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior Knowledge—0
Predicting and Understanding Law-Making with Word Vectors and an Ensemble Model—0
Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval—0
Using Word Embeddings for Automatic Query Expansion—0
NN-grams: Unifying neural network and n-gram language models for Speech Recognition—0
Gender and Interest Targeting for Sponsored Post Advertising at Tumblr—0
A segmental framework for fully-unsupervised large-vocabulary speech recognitionCode0
On Multiplicative Integration with Recurrent Neural Networks—0
Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015—0
Two Discourse Driven Language Models for Semantics—0
Bidirectional Long-Short Term Memory for Video Description—0
Watch What You Just Said: Image Captioning with Text-Conditional AttentionCode0
Learning to Generate Compositional Color DescriptionsCode0
Matching Networks for One Shot LearningCode1
MuFuRU: The Multi-Function Recurrent Unit—0
Sequence-to-Sequence Learning as Beam-Search OptimizationCode0
Gated Word-Character Recurrent Language ModelCode0
Zoneout: Regularizing RNNs by Randomly Preserving Hidden ActivationsCode1
Temporal Action Detection Using a Statistical Language ModelCode0
Enriching Cold Start Personalized Language Model Using Social Network Information—0
Embedding Senses for Efficient Graph-based Word Sense Disambiguation—0
Gender-Distinguishing Features in Film Dialogue—0
Towards Semantic-based Hybrid Machine Translation between Bulgarian and English—0
Model Combination for Correcting Preposition Selection Errors—0
TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling—0
UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports—0
UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation—0
UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment ClassificationCode0
Show:102550
← PrevPage 334 of 353Next →
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