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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16251–16300 of 17610 papers

TitleStatusHype
Improving Language Modelling with Noise-contrastive estimation—0
Sentence Correction Based on Large-scale Language Modelling—0
Dynamic Evaluation of Neural Sequence ModelsCode0
Language Modeling with Highway LSTM—0
Neural Networks for Text Correction and Completion in Keyboard Decoding—0
Learning Intrinsic Sparse Structures within Long Short-Term MemoryCode0
Empower Sequence Labeling with Task-Aware Neural Language ModelCode0
Shifting Mean Activation Towards Zero with Bipolar Activation FunctionsCode0
Language Models of Spoken Dutch—0
Cynical Selection of Language Model Training DataCode0
A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a DiscourseCode0
Language Modeling by Clustering with Word Embeddings for Text Readability Assessment—0
Understanding the Logical and Semantic Structure of Large Documents—0
Patterns versus Characters in Subword-aware Neural Language ModelingCode0
Predicting Pronouns with a Convolutional Network and an N-gram Model—0
Neural Machine Translation for Cross-Lingual Pronoun Prediction—0
The JHU Machine Translation Systems for WMT 2017—0
The Benefit of Syntactic vs. Linear N-grams for Linguistic Description—0
Template-Free Construction of Rhyming Poems with Thematic Cohesion—0
Synthetic Literature: Writing Science Fiction in a Co-Creative Process—0
Supersense Tagging with a Combination of Character, Subword, and Word-level Representations—0
Towards Compact and Fast Neural Machine Translation Using a Combined Method—0
Sub-character Neural Language Modelling in Japanese—0
Stylistic Variation in Television Dialogue for Natural Language Generation—0
Stack-based Multi-layer Attention for Transition-based Dependency Parsing—0
UHH Submission to the WMT17 Quality Estimation Shared Task—0
Towards Lexical Chains for Knowledge-Graph-based Word Embeddings—0
Sense-Aware Statistical Machine Translation using Adaptive Context-Dependent Clustering—0
LIMSI@WMT'17—0
Towards Quantum Language Models—0
Revising the METU-Sabanc Turkish Treebank: An Exercise in Surface-Syntactic Annotation of Agglutinative Languages—0
Memory Augmented Neural Networks for Natural Language Processing—0
Preserving Distributional Information in Dialogue Act Classification—0
Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences—0
Predictor-Estimator using Multilevel Task Learning with Stack Propagation for Neural Quality Estimation—0
PJIIT's systems for WMT 2017 Conference—0
Multi-Grained Chinese Word Segmentation—0
Transliterated Mobile Keyboard Input via Weighted Finite-State Transducers—0
Multi-source Neural Automatic Post-Editing: FBK's participation in the WMT 2017 APE shared task—0
Natural Language Descriptions for Human Activities in Video Streams—0
Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics—0
Byte-based Neural Machine Translation—0
Deciphering Related Languages—0
Evaluation of Finite State Morphological Analyzers Based on Paradigm Extraction from Wiktionary—0
Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction—0
A Dataset and Classifier for Recognizing Social Media English—0
Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models—0
Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution—0
Cross-lingual Character-Level Neural Morphological Tagging—0
Amharic-English Speech Translation in Tourism Domain—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