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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15051–15100 of 17610 papers

TitleStatusHype
Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue—0
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects—0
Pre-Training BERT on Domain Resources for Short Answer Grading—0
TILM: Neural Language Models with Evolving Topical Influence—0
Synthetic Propaganda Embeddings To Train A Linear Projection—0
Selecting, Planning, and Rewriting: A Modular Approach for Data-to-Document Generation and Translation—0
Jeff Da at COIN - Shared Task: BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge—0
Phonetic Normalization for Machine Translation of User Generated Content—0
Unsupervised Aspect-Based Multi-Document Abstractive Summarization—0
A Recurrent BERT-based Model for Question GenerationCode0
English-Myanmar Supervised and Unsupervised NMT: NICT's Machine Translation Systems at WAT-2019—0
FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder ParadigmCode0
Enhancing BERT for Lexical Normalization—0
Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking—0
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge—0
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning—0
GEM: Generative Enhanced Model for adversarial attacks—0
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering—0
Fine-Grained Propaganda Detection with Fine-Tuned BERT—0
Contextual Text Denoising with Masked Language Model—0
Divisive Language and Propaganda Detection using Multi-head Attention Transformers with Deep Learning BERT-based Language Models for Binary Classification—0
Context-Aware Neural Machine Translation Decoding—0
Hello, It's GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems—0
A neural document language modeling framework for spoken document retrieval—0
Multi-Stage Document Ranking with BERTCode1
Positional Attention-based Frame Identification with BERT: A Deep Learning Approach to Target Disambiguation and Semantic Frame Selection—0
Masked Language Model ScoringCode1
Learning to Customize Model Structures for Few-shot Dialogue Generation TasksCode0
Learning Deterministic Weighted Automata with Queries and CounterexamplesCode0
Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer—0
Contextual Text Denoising with Masked Language Models—0
Fill in the Blanks: Imputing Missing Sentences for Larger-Context Neural Machine Translation—0
Inducing brain-relevant bias in natural language processing modelsCode0
Learning Rich Image Region Representation for Visual Question Answering—0
Semi-Supervised Natural Language Approach for Fine-Grained Classification of Medical Reports—0
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M ProductsCode0
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social MediaCode0
Exploring Kernel Functions in the Softmax Layer for Contextual Word Classification—0
Sketch-Fill-A-R: A Persona-Grounded Chit-Chat Generation Framework—0
Thieves on Sesame Street! Model Extraction of BERT-based APIsCode0
HUBERT Untangles BERT to Improve Transfer across NLP TasksCode0
FineText: Text Classification via Attention-based Language Model Fine-tuning—0
SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering—0
On the Cross-lingual Transferability of Monolingual RepresentationsCode0
L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition—0
An Empirical Study of Efficient ASR Rescoring with Transformers—0
Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized RepresentationsCode0
Healthcare NER Models Using Language Model Pretraining—0
Efficient Dynamic WFST Decoding for Personalized Language Models—0
Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model—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