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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 17251–17300 of 17610 papers

TitleStatusHype
bs,hr,srWaC - Web Corpora of Bosnian, Croatian and Serbian—0
A Graph-Based Approach to String Regeneration—0
Comparing CRF and template-matching in phrasing tasks within a Hybrid MT system—0
A Principled Approach to Context-Aware Machine Translation—0
Dynamic Topic Adaptation for Phrase-based MT—0
Integrating an Unsupervised Transliteration Model into Statistical Machine Translation—0
Augmenting Translation Models with Simulated Acoustic Confusions for Improved Spoken Language Translation—0
Undirected Machine Translation with Discriminative Reinforcement Learning—0
Using idiolects and sociolects to improve word prediction—0
Word Ordering with Phrase-Based Grammars—0
Using Hypothesis Selection Based Features for Confusion Network MT System Combination—0
DeepWalk: Online Learning of Social RepresentationsCode0
Using Entropy Estimates for DAG-Based Ontologies—0
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding methodCode0
Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech RecognitionCode0
Learning to Predict from Textual Data—0
Assessing Wikipedia-Based Cross-Language Retrieval Models—0
Dynamic Language Models for Streaming Text—0
WoNeF, an improved, expanded and evaluated automatic French translation of WordNet—0
Locally Non-Linear Learning for Statistical Machine Translation via Discretization and Structured Regularization—0
Language Modeling with Power Low Rank Ensembles—0
How to Construct Deep Recurrent Neural Networks—0
Integrating Dictionary and Web N-grams for Chinese Spell Checking—0
Error Detection in Automatic Speech Recognition—0
Cumulative Progress in Language Models for Information Retrieval—0
Correcting Serial Grammatical Errors based on N-grams and Syntax—0
Training and Analysing Deep Recurrent Neural Networks—0
Regular Patterns - Probably Approximately Correct Language Model—0
Vietnamese Text Accent Restoration with Statistical Machine Translation—0
Chinese Spelling Checker Based on Statistical Machine Translation—0
An Unsupervised Parameter Estimation Algorithm for a Generative Dependency N-gram Language Model—0
An Online Algorithm for Learning over Constrained Latent Representations using Multiple Views—0
Improving Statistical Machine Translation with Word Class Models—0
機器翻譯為本的中文拼字改錯系統 (Chinese Spelling Checker Based on Statistical Machine Translation)—0
Chinese Spelling Check Evaluation at SIGHAN Bake-off 2013—0
An Empirical Study Of Semi-Supervised Chinese Word Segmentation Using Co-Training—0
Conditional Random Field-based Parser and Language Model for Tradi-tional Chinese Spelling Checker—0
An Efficient Language Model Using Double-Array Structures—0
Graph-Based Unsupervised Learning of Word Similarities Using Heterogeneous Feature Types—0
A Study of Language Modeling for Chinese Spelling Check—0
A Comparison of Centrality Measures for Graph-Based Keyphrase ExtractionCode0
Detecting Domain Dedicated Polar Words—0
Deriving Adjectival Scales from Continuous Space Word Representations—0
A Topic-Triggered Language Model for Statistical Machine Translation—0
Feature-based Neural Language Model and Chinese Word Segmentation—0
Dependency Language Models for Sentence Completion—0
Dependency-Based Decipherment for Resource-Limited Machine Translation—0
Improvements to the Bayesian Topic N-Gram Models—0
改良語句模型技術於節錄式語音摘要之研究 (Improved Sentence Modeling Techniques for Extractive Speech Summarization) [In Chinese]—0
Converting Continuous-Space Language Models into N-Gram Language Models for Statistical Machine Translation—0
Show:102550
← PrevPage 346 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