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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15451–15500 of 17610 papers

TitleStatusHype
Word-based Domain Adaptation for Neural Machine Translation—0
Selfie: Self-supervised Pretraining for Image EmbeddingCode0
Real or Fake? Learning to Discriminate Machine from Human Generated Text—0
From Caesar Cipher to Unsupervised Learning: A New Method for Classifier Parameter Estimation—0
Generating Question-Answer HierarchiesCode0
Efficient, Lexicon-Free OCR using Deep Learning—0
An Imitation Learning Approach to Unsupervised ParsingCode0
How multilingual is Multilingual BERT?Code1
Improving Neural Language Models by Segmenting, Attending, and Predicting the FutureCode0
The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error CorrectionCode0
Training Neural Response Selection for Task-Oriented Dialogue SystemsCode0
Finding Syntactic Representations in Neural StacksCode0
Better Character Language Modeling Through Morphology—0
A Semi-Supervised Approach for Low-Resourced Text GenerationCode0
Does It Make Sense? And Why? A Pilot Study for Sense Making and ExplanationCode1
Pre-training of Graph Augmented Transformers for Medication RecommendationCode0
MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFit—0
Investigating Speech Recognition for Improving Predictive AAC—0
Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model—0
Noisy Neural Language Modeling for Typing Prediction in BCI Communication—0
What a neural language model tells us about spatial relationsCode0
Entity Decisions in Neural Language Modelling: Approaches and Problems—0
Discriminating between Mandarin Chinese and Swiss-German varieties using adaptive language models—0
Cross-lingual Subjectivity Detection for Resource Lean Languages—0
Enabling Real-time Neural IME with Incremental Vocabulary Selection—0
A Partially Rule-Based Approach to AMR Generation—0
Beyond Context: A New Perspective for Word Embeddings—0
How to Avoid Sentences Spelling Boring? Towards a Neural Approach to Unsupervised Metaphor Generation—0
ColumbiaNLP at SemEval-2019 Task 8: The Answer is Language Model Fine-tuning—0
Dick-Preston and Morbo at SemEval-2019 Task 4: Transfer Learning for Hyperpartisan News Detection—0
Columbia at SemEval-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification—0
CLP at SemEval-2019 Task 3: Multi-Encoder in Hierarchical Attention Networks for Contextual Emotion Detection—0
SEQ\^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence CompressionCode0
Serial Recall Effects in Neural Language Modeling—0
Rethinking Complex Neural Network Architectures for Document ClassificationCode0
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines—0
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment—0
Neural GRANNy at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction—0
Multilingual prediction of Alzheimer's disease through domain adaptation and concept-based language modelling—0
nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection—0
Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples—0
UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language—0
Adversarial Generation and Encoding of Nested Texts—0
Learning to Generate Grounded Visual Captions without Localization SupervisionCode1
Table2Vec: Neural Word and Entity Embeddings for Table Population and RetrievalCode0
A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension—0
A Compare-Aggregate Model with Latent Clustering for Answer Selection—0
Lattice-based lightly-supervised acoustic model training—0
LANGUAGE MODEL EMBEDDINGS IMPROVE SENTIMENT ANALYSIS IN RUSSIANCode0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—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