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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15301–15350 of 17610 papers

TitleStatusHype
Restricted Recurrent Neural NetworksCode0
Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical TextCode0
Latent Relation Language Models—0
WikiCREM: A Large Unsupervised Corpus for Coreference ResolutionCode0
LXMERT: Learning Cross-Modality Encoder Representations from TransformersCode1
Universal Adversarial Triggers for Attacking and Analyzing NLPCode0
Encoder-Agnostic Adaptation for Conditional Language GenerationCode0
Question Answering based Clinical Text Structuring Using Pre-trained Language Model—0
Musical Rhythm Transcription Based on Bayesian Piece-Specific Score Models Capturing Repetitions—0
Parsimonious Morpheme Segmentation with an Application to Enriching Word Embeddings—0
EmotionX-IDEA: Emotion BERT -- an Affectional Model for ConversationCode0
Language Features Matter: Effective Language Representations for Vision-Language Tasks—0
Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting—0
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training—0
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring—0
SenseBERT: Driving Some Sense into BERT—0
Visualizing and Understanding the Effectiveness of BERT—0
Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation—0
SG-Net: Syntax-Guided Machine Reading ComprehensionCode0
On The Evaluation of Machine Translation Systems Trained With Back-TranslationCode0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding—0
Predicting 3D Human Dynamics from VideoCode0
Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning—0
An Effective Domain Adaptive Post-Training Method for BERT in Response SelectionCode0
TAPER: Time-Aware Patient EHR RepresentationCode0
Multi-modality Latent Interaction Network for Visual Question Answering—0
Unsupervised Stemming based Language Model for Telugu Broadcast News Transcription—0
VisualBERT: A Simple and Performant Baseline for Vision and LanguageCode1
On the Variance of the Adaptive Learning Rate and BeyondCode1
Clustering of Deep Contextualized Representations for Summarization of Biomedical TextsCode0
Exploring Neural Net Augmentation to BERT for Question Answering on SQUAD 2.0—0
Self-Knowledge Distillation in Natural Language Processing—0
Deep learning languages: a key fundamental shift from probabilities to weights?—0
GTCOM Neural Machine Translation Systems for WMT19—0
CUED@WMT19:EWC\&LMs—0
DBMS-KU Interpolation for WMT19 News Translation Task—0
The RWTH Aachen University Machine Translation Systems for WMT 2019—0
The LMU Munich Unsupervised Machine Translation System for WMT19—0
Visualizing RNN States with Predictive Semantic Encodings—0
ZCU-NLP at MADAR 2019: Recognizing Arabic Dialects—0
The SMarT Classifier for Arabic Fine-Grained Dialect Identification—0
JHU System Description for the MADAR Arabic Dialect Identification Shared Task—0
Simple Construction of Mixed-Language Texts for Vocabulary Learning—0
KU\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI—0
Team JUST at the MADAR Shared Task on Arabic Fine-Grained Dialect Identification—0
Arabic Dialect Identification for Travel and Twitter Text—0
hULMonA: The Universal Language Model in ArabicCode0
Finding Hierarchical Structure in Neural Stacks Using Unsupervised ParsingCode0
ArbDialectID at MADAR Shared Task 1: Language Modelling and Ensemble Learning for Fine Grained Arabic Dialect Identification—0
An LSTM Adaptation Study of (Un)grammaticalityCode0
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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