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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection—0
Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge—0
Character and Subword-Based Word Representation for Neural Language Modeling Prediction—0
Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models—0
Byte-based Neural Machine Translation—0
Evaluation of Finite State Morphological Analyzers Based on Paradigm Extraction from Wiktionary—0
Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution—0
Amharic-English Speech Translation in Tourism Domain—0
Bilexical Embeddings for Quality Estimation—0
Connecting the Dots: Towards Human-Level Grammatical Error Correction—0
A Dataset and Classifier for Recognizing Social Media English—0
LIMSI@WMT'17—0
Supersense Tagging with a Combination of Character, Subword, and Word-level Representations—0
Predicting Pronouns with a Convolutional Network and an N-gram Model—0
Multi-source Neural Automatic Post-Editing: FBK's participation in the WMT 2017 APE shared task—0
Sub-character Neural Language Modelling in Japanese—0
Stylistic Variation in Television Dialogue for Natural Language Generation—0
The Benefit of Syntactic vs. Linear N-grams for Linguistic Description—0
Predictor-Estimator using Multilevel Task Learning with Stack Propagation for Neural Quality Estimation—0
Sense-Aware Statistical Machine Translation using Adaptive Context-Dependent Clustering—0
Synthetic Literature: Writing Science Fiction in a Co-Creative Process—0
Natural Language Descriptions for Human Activities in Video Streams—0
The JHU Machine Translation Systems for WMT 2017—0
Neural Machine Translation for Cross-Lingual Pronoun Prediction—0
PJIIT's systems for WMT 2017 Conference—0
Template-Free Construction of Rhyming Poems with Thematic Cohesion—0
Stack-based Multi-layer Attention for Transition-based Dependency Parsing—0
Towards Quantum Language Models—0
Multi-Grained Chinese Word Segmentation—0
Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences—0
Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics—0
Towards Compact and Fast Neural Machine Translation Using a Combined Method—0
Preserving Distributional Information in Dialogue Act Classification—0
Using Target-side Monolingual Data for Neural Machine Translation through Multi-task Learning—0
Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding—0
Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora—0
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions—0
Unsupervised Pretraining for Sequence to Sequence Learning—0
Cross-lingual Character-Level Neural Morphological Tagging—0
Deciphering Related Languages—0
Identifying Humor in Reviews using Background Text Sources—0
Integrating Order Information and Event Relation for Script Event Prediction—0
Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources—0
Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic—0
Glyph-aware Embedding of Chinese CharactersCode0
Gradual Learning of Recurrent Neural NetworksCode0
A Study on Neural Network Language Modeling—0
Long-Short Range Context Neural Networks for Language Modeling—0
Cold Fusion: Training Seq2Seq Models Together with Language Models—0
The Microsoft 2017 Conversational Speech Recognition System—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