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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 17051–17100 of 17610 papers

TitleStatusHype
Embedding Word Similarity with Neural Machine Translation—0
A Simple and Efficient Method To Generate Word Sense Representations—0
Deep Structured Output Learning for Unconstrained Text Recognition—0
Skip-gram Language Modeling Using Sparse Non-negative Matrix Probability Estimation—0
使用概念資訊於中文大詞彙連續語音辨識之研究 (Exploring Concept Information for Mandarin Large Vocabulary Continuous Speech Recognition) [In Chinese]—0
A Hierarchical Word Sequence Language Model—0
Incrementally Updating the SMT Reordering Model—0
Modeling Structural Topic Transitions for Automatic Lyrics Generation—0
Transition-based Knowledge Graph Embedding with Relational Mapping Properties—0
Zero-Shot Learning of Language Models for Describing Human Actions Based on Semantic Compositionality of Actions—0
Extracting and Selecting Relevant Corpora for Domain Adaptation in MT—0
LMSim : Computing Domain-specific Semantic Word Similarities Using a Language Modeling Approach—0
Applications of Lexicographic Semirings to Problems in Speech and Language Processing—0
From Captions to Visual Concepts and BackCode0
Unifying Visual-Semantic Embeddings with Multimodal Neural Language ModelsCode0
The Effect of Dependency Representation Scheme on Syntactic Language Modelling—0
Leveraging known Semantics for Spelling Correction—0
A random forest system combination approach for error detection in digital dictionaries—0
Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling—0
Large Vocabulary Arabic Online Handwriting Recognition System—0
Expanding the Language model in a low-resource hybrid MT system—0
Chinese Spelling Error Detection and Correction Based on Language Model, Pronunciation, and Shape—0
運用概念模型化技術於中文大詞彙連續語音辨識之語言模型調適 (Leveraging Concept Modeling Techniques for Language Model Adaptation in Mandarin Large Vocabulary Continuous Speech Recognition) [In Chinese]—0
探究新穎語句模型化技術於節錄式語音摘要 (Investigating Novel Sentence Modeling Techniques for Extractive Speech Summarization) [In Chinese]—0
Japanese to English Machine Translation using Preordering and Compositional Distributed Semantics—0
A machine translation system combining rule-based machine translation and statistical post-editing—0
Forest-to-String SMT for Asian Language Translation: NAIST at WAT 2014—0
Weblio Pre-reordering Statistical Machine Translation System—0
Language variety identification in Spanish tweets—0
Exploration of the Impact of Maximum Entropy in Recurrent Neural Network Language Models for Code-Switching Speech—0
A Pipeline Approach to Supervised Error Correction for the QALB-2014 Shared Task—0
GWU-HASP: Hybrid Arabic Spelling and Punctuation Corrector—0
Automatic Correction of Arabic Text: a Cascaded Approach—0
CMUQ@QALB-2014: An SMT-based System for Automatic Arabic Error Correction—0
Dependency-Based Bilingual Language Models for Reordering in Statistical Machine Translation—0
Improved Decipherment of Homophonic Ciphers—0
Comparing Representations of Semantic Roles for String-To-Tree Decoding—0
Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks—0
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation—0
Joint Decoding of Tree Transduction Models for Sentence Compression—0
Neural Network Based Bilingual Language Model Growing for Statistical Machine Translation—0
Leveraging Effective Query Modeling Techniques for Speech Recognition and Summarization—0
Language Modeling with Functional Head Constraint for Code Switching Speech Recognition—0
Morphological Segmentation for Keyword Spotting—0
The Inside-Outside Recursive Neural Network model for Dependency Parsing—0
Jointly Learning Word Representations and Composition Functions Using Predicate-Argument Structures—0
PCFG Induction for Unsupervised Parsing and Language Modelling—0
Submodularity for Data Selection in Machine Translation—0
Composition of Word Representations Improves Semantic Role Labelling—0
Exact Decoding for Phrase-Based Statistical Machine Translation—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