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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16601–16650 of 17610 papers

TitleStatusHype
Multiplicative LSTM for sequence modellingCode0
Language as a Latent Variable: Discrete Generative Models for Sentence Compression—0
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities—0
The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition—0
Advances in All-Neural Speech Recognition—0
Characterizing the Language of Online Communities and its Relation to Community Reception—0
Character-Level Language Modeling with Hierarchical Recurrent Neural Networks—0
The Microsoft 2016 Conversational Speech Recognition System—0
Purely sequence-trained neural networks for ASR based on lattice-free MMI—0
Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks—0
Hierarchical Multiscale Recurrent Neural NetworksCode0
PMI Matrix Approximations with Applications to Neural Language Modeling—0
Lexical-Morphological Modeling for Legal Text Analysis—0
LVCSR System on a Hybrid GPU-CPU Embedded Platform for Real-Time Dialog Applications—0
Generating Paraphrases from DBPedia using Deep Learning—0
Human-like Natural Language Generation Using Monte Carlo Tree Search—0
Enabling text readability awareness during the micro planning phase of NLG applications—0
An Analysis of the Ability of Statistical Language Models to Capture the Structural Properties of Language—0
Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions—0
Which techniques does your application use?: An information extraction framework for scientific articles—0
Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix TreesCode0
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC—0
Numerically Grounded Language Models for Semantic Error Correction—0
Generative Knowledge Transfer for Neural Language Models—0
A deep language model for software codeCode0
Cross-lingual projection for class-based language models—0
A Linear Baseline Classifier for Cross-Lingual Pronoun Prediction—0
Chinese Couplet Generation with Neural Network Structures—0
English-Portuguese Biomedical Translation Task Using a Genuine Phrase-Based Statistical Machine Translation Approach—0
Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks—0
Cross-lingual Pronoun Prediction for English, French and German with Maximum Entropy Classification—0
Character-based Neural Machine Translation—0
Babler - Data Collection from the Web to Support Speech Recognition and Keyword Search—0
Improving cross-domain n-gram language modelling with skipgrams—0
It-disambiguation and source-aware language models for cross-lingual pronoun prediction—0
A Neural Knowledge Language Model—0
Argumentative texts and clause types—0
Exponentially Decaying Bag-of-Words Input Features for Feed-Forward Neural Network in Statistical Machine Translation—0
Implicit Semantic Roles in a Multilingual Setting—0
DFKI's system for WMT16 IT-domain task, including analysis of systematic errors—0
Decomposing Bilexical Dependencies into Semantic and Syntactic Vectors—0
Cross-lingual Pronoun Prediction with Linguistically Informed Features—0
Hybrid Morphological Segmentation for Phrase-Based Machine Translation—0
Edinburgh's Statistical Machine Translation Systems for WMT16—0
Abu-MaTran at WMT 2016 Translation Task: Deep Learning, Morphological Segmentation and Tuning on Character Sequences—0
CobaltF: A Fluent Metric for MT Evaluation—0
Distributed representation and estimation of WFST-based n-gram models—0
WMT 2016 Multimodal Translation System Description based on Bidirectional Recurrent Neural Networks with Double-Embeddings—0
Word Alignment without NULL Words—0
Unsupervised morph segmentation and statistical language models for vocabulary expansion—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