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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14601–14650 of 17610 papers

TitleStatusHype
Rank and run-time aware compression of NLP Applications—0
Linguistic Profiling of a Neural Language Model—0
Inference Strategies for Machine Translation with Conditional Masking—0
Acrostic Poem Generation—0
When in Doubt, Ask: Generating Answerable and Unanswerable Questions, UnsupervisedCode0
NLP Service APIs and Models for Efficient Registration of New Clients—0
Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles—0
Syntax Representation in Word Embeddings and Neural Networks -- A Survey—0
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding—0
SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval—0
Multi-Reward based Reinforcement Learning for Neural Machine Translation—0
Low-Resource Text Classification via Cross-lingual Language Model Fine-tuning—0
Chinese Long and Short Form Choice Exploiting Neural Network Language Modeling Approaches—0
Detecting White Supremacist Hate Speech using Domain Specific Word Embedding with Deep Learning and BERT—0
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds—0
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-trainingCode0
How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text—0
A Novel Joint Framework for Multiple Chinese Events Extraction—0
Entity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction—0
WAE_RN: Integrating Wasserstein Autoencoder and Relational Network for Text Sequence—0
Unsupervised Melody Segmentation Based on a Nested Pitman-Yor Language Model—0
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text—0
The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning—0
Improving Low Compute Language Modeling with In-Domain Embedding InitialisationCode0
Deep Transformers with Latent DepthCode0
Distillation of Weighted Automata from Recurrent Neural Networks using a Spectral Approach—0
VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation—0
PIN: A Novel Parallel Interactive Network for Spoken Language Understanding—0
Quantal synaptic dilution enhances sparse encoding and dropout regularisation in deep networks—0
Multi-timescale Representation Learning in LSTM Language Models—0
Inductive Graph Embeddings through Locality Encodings—0
RecoBERT: A Catalog Language Model for Text-Based Recommendations—0
Toward a Thermodynamics of MeaningCode0
AnchiBERT: A Pre-Trained Model for Ancient ChineseLanguage Understanding and GenerationCode0
WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis using Transformers—0
BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition—0
COMET: A Neural Framework for MT Evaluation—0
Hierarchical GPT with Congruent Transformers for Multi-Sentence Language Models—0
GraphCodeBERT: Pre-training Code Representations with Data Flow—0
DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition ExtractionCode0
Towards Fully 8-bit Integer Inference for the Transformer Model—0
Retrofitting Structure-aware Transformer Language Model for End Tasks—0
Automated Source Code Generation and Auto-completion Using Deep Learning: Comparing and Discussing Current Language-Model-Related ApproachesCode0
Generative Language-Grounded Policy in Vision-and-Language Navigation with Bayes' Rule—0
DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT—0
Cascaded Semantic and Positional Self-Attention Network for Document Classification—0
MLMLM: Link Prediction with Mean Likelihood Masked Language Model—0
Differentially Private Language Models Benefit from Public Pre-training—0
Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding—0
Dialogue-adaptive Language Model Pre-training From Quality EstimationCode0
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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