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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 17351–17400 of 17610 papers

TitleStatusHype
Lexical Selection for Hybrid MT with Sequence Labeling—0
The CMU Machine Translation Systems at WMT 2013: Syntax, Synthetic Translation Options, and Pseudo-References—0
The University of Cambridge Russian-English System at WMT13—0
The (Un)faithful Machine Translator—0
Sub-lexical Dialogue Act Classification in a Spoken Dialogue System Support for the Elderly with Cognitive Disabilities—0
Structure Learning in Weighted Languages—0
Temporal classification for historical Romanian texts—0
Letter N-Gram-based Input Encoding for Continuous Space Language Models—0
Language-independent hybrid MT with PRESEMT—0
LIMSI @ WMT13—0
Joint WMT 2013 Submission of the QUAERO Project—0
IRISA participation to BioNLP-ST13: lazy-learning and information retrieval for information extraction tasks—0
NAIST at 2013 CoNLL Grammatical Error Correction Shared Task—0
Grammatical Error Correction as Multiclass Classification with Single Model—0
A Tree Transducer Model for Grammatical Error Correction—0
UM-Checker: A Hybrid System for English Grammatical Error Correction—0
Sentence Compression with Joint Structural Inference—0
Better Word Representations with Recursive Neural Networks for Morphology—0
Accurate Word Segmentation using Transliteration and Language Model Projection—0
Cross-lingual Transfer of Semantic Role Labeling Models—0
Explicit and Implicit Syntactic Features for Text Classification—0
Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation—0
Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation—0
Improving Text Simplification Language Modeling Using Unsimplified Text Data—0
Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?—0
A Tightly-coupled Unsupervised Clustering and Bilingual Alignment Model for Transliteration—0
Additive Neural Networks for Statistical Machine Translation—0
Cut the noise: Mutually reinforcing reordering and alignments for improved machine translation—0
Graph Propagation for Paraphrasing Out-of-Vocabulary Words in Statistical Machine Translation—0
Beam Search for Solving Substitution Ciphers—0
Integrating Phrase-based Reordering Features into a Chart-based Decoder for Machine Translation—0
Derivational Smoothing for Syntactic Distributional Semantics—0
An Infinite Hierarchical Bayesian Model of Phrasal Translation—0
Combination of Recurrent Neural Networks and Factored Language Models for Code-Switching Language Modeling—0
Using Context Vectors in Improving a Machine Translation System with Bridge Language—0
Word surprisal predicts N400 amplitude during reading—0
Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition—0
Learning Non-linear Features for Machine Translation Using Gradient Boosting Machines—0
Semantic Parsing as Machine Translation—0
Modeling of term-distance and term-occurrence information for improving n-gram language model performance—0
Punctuation Prediction with Transition-based Parsing—0
PATHS: A System for Accessing Cultural Heritage Collections—0
Learning to Prune: Context-Sensitive Pruning for Syntactic MT—0
Re-embedding words—0
Scalable Modified Kneser-Ney Language Model Estimation—0
Task Alternation in Parallel Sentence Retrieval for Twitter Translation—0
Smoothed marginal distribution constraints for language modeling—0
A Shift-Reduce Parsing Algorithm for Phrase-based String-to-Dependency Translation—0
Decipherment Complexity in 1:1 Substitution Ciphers—0
A Context Free TAG Variant—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