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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 17301–17350 of 17610 papers

TitleStatusHype
A Log-Linear Model for Unsupervised Text Normalization—0
Efficient Left-to-Right Hierarchical Phrase-Based Translation with Improved Reordering—0
Bootstrapping Phrase-based Statistical Machine Translation via WSD Integration—0
Introduction to CKIP Chinese Spelling Check System for SIGHAN Bakeoff 2013 Evaluation—0
Exploiting Language Models for Visual Recognition—0
Decoding with Large-Scale Neural Language Models Improves Translation—0
Decipherment with a Million Random Restarts—0
Event-Centered Information Retrieval Using Kernels on Event Graphs—0
A Hybrid Chinese Spelling Correction Using Language Model and Statistical Machine Translation with Reranking—0
A Hierarchical Entity-Based Approach to Structuralize User Generated Content in Social Media: A Case of Yahoo! Answers—0
Recurrent Continuous Translation Models—0
TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction—0
Mapping Rules for Building a Tunisian Dialect Lexicon and Generating Corpora—0
Joint Learning of Phonetic Units and Word Pronunciations for ASR—0
Translating Chinese Unknown Words by Automatically Acquired Templates—0
Multimodal Comparable Corpora as Resources for Extracting Parallel Data: Parallel Phrases Extraction—0
Semantic v.s. Positions: Utilizing Balanced Proximity in Language Model Smoothing for Information Retrieval—0
KySS 1.0: a Framework for Automatic Evaluation of Chinese Input Method Engines—0
The AI-KU System at the SPMRL 2013 Shared Task : Unsupervised Features for Dependency ParsingCode0
On the Effectiveness of Using Syntactic and Shallow Semantic Tree Kernels for Automatic Assessment of Essays—0
Parser Accuracy in Quality Estimation of Machine Translation: A Tree Kernel Approach—0
Supervised Sentence Fusion with Single-Stage Inference—0
Joint Language and Translation Modeling with Recurrent Neural Networks—0
Structured Penalties for Log-Linear Language Models—0
Source and Translation Classification using Most Frequent Words—0
Semantic Na\" Bayes Classifier for Document Classification—0
Predicate Logic as a Modeling Language: Modeling and Solving some Machine Learning and Data Mining Problems with IDP3—0
Combining, Adapting and Reusing Bi-texts between Related Languages: Application to Statistical Machine Translation (invited talk)—0
History Based Unsupervised Data Oriented Parsing—0
Improving Language Model Adaptation using Automatic Data Selection and Neural Network—0
Edit Distance: A New Data Selection Criterion for Domain Adaptation in SMT—0
Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation—0
Explicit and Implicit Syntactic Features for Text Classification—0
Graph Propagation for Paraphrasing Out-of-Vocabulary Words in Statistical Machine Translation—0
Hybrid Selection of Language Model Training Data Using Linguistic Information and Perplexity—0
A Comparison of Smoothing Techniques for Bilingual Lexicon Extraction from Comparable Corpora—0
Combination of Recurrent Neural Networks and Factored Language Models for Code-Switching Language Modeling—0
Answer Extraction by Recursive Parse Tree Descent—0
Investigations in Exact Inference for Hierarchical Translation—0
Distortion Model Considering Rich Context for Statistical Machine Translation—0
DCU-Symantec at the WMT 2013 Quality Estimation Shared Task—0
A Tree Transducer Model for Grammatical Error Correction—0
GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts—0
Decipherment Complexity in 1:1 Substitution Ciphers—0
IRISA participation to BioNLP-ST13: lazy-learning and information retrieval for information extraction tasks—0
A Robotic Agent in a Virtual Environment that Performs Situated Incremental Understanding of Navigational Utterances—0
A Shift-Reduce Parsing Algorithm for Phrase-based String-to-Dependency Translation—0
An Infinite Hierarchical Bayesian Model of Phrasal Translation—0
A Tightly-coupled Unsupervised Clustering and Bilingual Alignment Model for Transliteration—0
Generating English Determiners in Phrase-Based Translation with Synthetic Translation Options—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