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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16951–17000 of 17610 papers

TitleStatusHype
Automatic Identification of Rhetorical Questions—0
Word Vector/Conditional Random Field-based Chinese Spelling Error Detection for SIGHAN-2015 Evaluation—0
Vector-space calculation of semantic surprisal for predicting word pronunciation duration—0
Unsupervised Prediction of Acceptability Judgements—0
Using word embedding for bio-event extraction—0
Tackling Sparsity, the Achilles Heel of Social Networks: Language Model Smoothing via Social Regularization—0
Tibetan Unknown Word Identification from News Corpora for Supporting Lexicon-based Tibetan Word Segmentation—0
The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models—0
Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction—0
Reducing infrequent-token perplexity via variational corpora—0
Toward Tweets Normalization Using Maximum Entropy—0
QCRI@QALB-2015 Shared Task: Correction of Arabic Text for Native and Non-Native Speakers' Errors—0
QCMUQ@QALB-2015 Shared Task: Combining Character level MT and Error-tolerant Finite-State Recognition for Arabic Spelling Correction—0
Passive and Pervasive Use of Bilingual Dictionary in Statistical Machine Translation—0
Trans-dimensional Random Fields for Language Modeling—0
Non-Linear Text Regression with a Deep Convolutional Neural Network—0
Neural Network Transduction Models in Transliteration Generation—0
Multi-system machine translation using online APIs for English-LatvianCode0
Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints—0
Learning Lexical Embeddings with Syntactic and Lexicographic Knowledge—0
Learning Cross-lingual Word Embeddings via Matrix Co-factorization—0
Language Identification and Modeling in Specialized Hardware—0
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals—0
Recognize Foreign Low-Frequency Words with Similar Pairs—0
Author Identification using Multi-headed Recurrent Neural NetworksCode0
A Bayesian Model for Generative Transition-based Dependency Parsing—0
Modeling Order in Neural Word Embeddings at Scale—0
Personalizing Universal Recurrent Neural Network Language Model with User Characteristic Features by Social Network Crowdsouring—0
A Hybrid Model for Enhancing Lexical Statistical Machine Translation (SMT)—0
Audience size and contextual effects on information density in Twitter conversations—0
Graph-based Coherence Modeling For Assessing Readability—0
Candidate evaluation strategies for improved difficulty prediction of language tests—0
Cache-Augmented Latent Topic Language Models for Speech Retrieval—0
Dependency Link Embeddings: Continuous Representations of Syntactic Substructures—0
Morpho-syntactic Regularities in Continuous Word Representations: A multilingual study.—0
Leveraging Preposition Ambiguity to Assess Compositional Distributional Models of Semantics—0
Modeling fMRI time courses with linguistic structure at various grain sizes—0
Predicting Prepositions for SMT—0
UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification—0
Voltron: A Hybrid System For Answer Validation Based On Lexical And Distance Features—0
Utility-based evaluation metrics for models of language acquisition: A look at speech segmentation—0
Vector Space Models for Scientific Document Summarization—0
Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme Conversion—0
The IBM 2015 English Conversational Telephone Speech Recognition System—0
Location Prediction of Social Images via Generative Model—0
Language Models for Image Captioning: The Quirks and What Works—0
Sequence to Sequence -- Video to TextCode0
Highway NetworksCode0
Fast and Accurate Preordering for SMT using Neural Networks—0
An Incremental Algorithm for Transition-based CCG Parsing—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