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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
The AI-KU System at the SPMRL 2013 Shared Task : Unsupervised Features for Dependency ParsingCode0
Introduction to CKIP Chinese Spelling Check System for SIGHAN Bakeoff 2013 Evaluation—0
Chinese Spelling Check Evaluation at SIGHAN Bake-off 2013—0
A Study of Language Modeling for Chinese Spelling Check—0
Chinese Spelling Checker Based on Statistical Machine Translation—0
A Hybrid Chinese Spelling Correction Using Language Model and Statistical Machine Translation with Reranking—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
Improving Statistical Machine Translation with Word Class Models—0
Converting Continuous-Space Language Models into N-Gram Language Models for Statistical Machine Translation—0
A Hierarchical Entity-Based Approach to Structuralize User Generated Content in Social Media: A Case of Yahoo! Answers—0
Exploiting Language Models for Visual Recognition—0
Efficient Left-to-Right Hierarchical Phrase-Based Translation with Improved Reordering—0
Deriving Adjectival Scales from Continuous Space Word Representations—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
An Empirical Study Of Semi-Supervised Chinese Word Segmentation Using Co-Training—0
A Log-Linear Model for Unsupervised Text Normalization—0
Decipherment with a Million Random Restarts—0
Decoding with Large-Scale Neural Language Models Improves Translation—0
Joint Language and Translation Modeling with Recurrent Neural Networks—0
Joint Learning of Phonetic Units and Word Pronunciations for ASR—0
Structured Penalties for Log-Linear Language Models—0
Recurrent Continuous Translation Models—0
Predicate Logic as a Modeling Language: Modeling and Solving some Machine Learning and Data Mining Problems with IDP3—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
History Based Unsupervised Data Oriented Parsing—0
Combining, Adapting and Reusing Bi-texts between Related Languages: Application to Statistical Machine Translation (invited talk)—0
Generating Sequences With Recurrent Neural NetworksCode1
Integrating morpho-syntactic features in English-Arabic statistical machine translation—0
Edinburgh's Machine Translation Systems for European Language Pairs—0
Generating English Determiners in Phrase-Based Translation with Synthetic Translation Options—0
A Robotic Agent in a Virtual Environment that Performs Situated Incremental Understanding of Navigational Utterances—0
A Comparison of Smoothing Techniques for Bilingual Lexicon Extraction from Comparable Corpora—0
A Phrase Orientation Model for Hierarchical Machine Translation—0
DCU-Symantec at the WMT 2013 Quality Estimation Shared Task—0
Feature Decay Algorithms for Fast Deployment of Accurate Statistical Machine Translation Systems—0
Building bilingual lexicon to create Dialect Tunisian corpora and adapt language model—0
Hybrid Selection of Language Model Training Data Using Linguistic Information and Perplexity—0
GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts—0
An MT Error-Driven Discriminative Word Lexicon using Sentence Structure Features—0
Automating speech reception threshold measurements using automatic speech recognition—0
Answer Extraction by Recursive Parse Tree Descent—0
T\"UB\.ITAK-B\.ILGEM German-English Machine Translation Systems for W13—0
Tunable Distortion Limits and Corpus Cleaning for SMT—0
Uses of Monolingual In-Domain Corpora for Cross-Domain Adaptation with Hybrid MT Approaches—0
The RWTH Aachen Machine Translation System for WMT 2013—0
Investigations in Exact Inference for Hierarchical 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