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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16301–16350 of 17610 papers

TitleStatusHype
Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources—0
Bilexical Embeddings for Quality Estimation—0
Integrating Order Information and Event Relation for Script Event Prediction—0
Generating Image Descriptions using Multilingual Data—0
Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic—0
Character-based recurrent neural networks for morphological relational reasoning—0
Improving Machine Translation Quality Estimation with Neural Network Features—0
Connecting the Dots: Towards Human-Level Grammatical Error Correction—0
Character and Subword-Based Word Representation for Neural Language Modeling Prediction—0
Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge—0
GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection—0
Identifying Humor in Reviews using Background Text Sources—0
What are the limitations on the flux of syntactic dependencies? Evidence from UD treebanks—0
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions—0
Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding—0
What do we need to know about an unknown word when parsing German—0
Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora—0
Unsupervised Pretraining for Sequence to Sequence Learning—0
Using Target-side Monolingual Data for Neural Machine Translation through Multi-task Learning—0
University of Rochester WMT 2017 NMT System Submission—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
The Microsoft 2017 Conversational Speech Recognition System—0
Cold Fusion: Training Seq2Seq Models Together with Language Models—0
Neural Networks Compression for Language Modeling—0
Modelling Word Burstiness in Natural Language: A Generalised Polya Process for Document Language Models in Information Retrieval—0
CLaC @ QATS: Quality Assessment for Text Simplification—0
Syllable-level Neural Language Model for Agglutinative Language—0
Comparison of Decoding Strategies for CTC Acoustic Models—0
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic SegmentationCode0
Early Improving Recurrent Elastic Highway Network—0
Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language ModelsCode0
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search—0
TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References—0
Revisiting Activation Regularization for Language RNNs—0
Dynamic Entity Representations in Neural Language ModelsCode0
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention—0
A Joint Model for Semantic Sequences: Frames, Entities, Sentiments—0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
Detecting Anxiety through RedditCode0
A Generative Parser with a Discriminative Recognition Algorithm—0
A Semi-universal Pipelined Approach to the CoNLL 2017 UD Shared Task—0
Learning Word Representations with Regularization from Prior Knowledge—0
Parsing with Context Embeddings—0
Neural Sequence-to-sequence Learning of Internal Word Structure—0
Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.—0
Representing Compositionality based on Multiple Timescales Gated Recurrent Neural Networks with Adaptive Temporal Hierarchy for Character-Level Language Models—0
UWAV at SemEval-2017 Task 7: Automated feature-based system for locating puns—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