SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 1455114600 of 17610 papers

TitleStatusHype
Segatron: Segment-aware Transformer for Language Modeling and Understanding0
Position Masking for Language Models0
FlauBERT : des mod\`eles de langue contextualis\'es pr\'e-entra\^ \'es pour le fran (FlauBERT : Unsupervised Language Model Pre-training for French)Code0
Contextualized French Language Models for Biomedical Named Entity Recognition0
An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features0
LRG at SemEval-2020 Task 7: Assessing the Ability of BERT and Derivative Models to Perform Short-Edits based Humor Grading0
Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLPCode1
Language Models are Few-Shot LearnersCode3
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion0
TIME: Text and Image Mutual-Translation Adversarial Networks0
Syntactic Structure Distillation Pretraining For Bidirectional Encoders0
Self-Training for Unsupervised Parsing with PRPN0
qDKT: Question-centric Deep Knowledge Tracing0
When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications0
Improving Segmentation for Technical Support ProblemsCode0
L2R2: Leveraging Ranking for Abductive ReasoningCode1
Living Machines: A study of atypical animacyCode0
Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning0
Text-to-Text Pre-Training for Data-to-Text TasksCode1
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition0
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems0
SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search0
Investigation of Large-Margin Softmax in Neural Language Modeling0
BERTweet: A pre-trained language model for English TweetsCode1
Early Stage LM Integration Using Local and Global Log-Linear Combination0
Improving Proper Noun Recognition in End-to-End ASR By Customization of the MWER Loss Criterion0
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text0
Human Sentence Processing: Recurrence or Attention?Code1
Table Search Using a Deep Contextualized Language ModelCode1
Iterative Pseudo-Labeling for Speech RecognitionCode0
The NTNU System at the Interspeech 2020 Non-Native Children's Speech ASR Challenge0
Yseop at SemEval-2020 Task 5: Cascaded BERT Language Model for Counterfactual Statement Analysis0
Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems0
GPT-too: A language-model-first approach for AMR-to-text generationCode1
How much complexity does an RNN architecture need to learn syntax-sensitive dependencies?Code0
Conformer: Convolution-augmented Transformer for Speech RecognitionCode3
Towards classification parity across cohorts0
MicroNet for Efficient Language ModelingCode1
Spelling Error Correction with Soft-Masked BERTCode1
Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model0
Challenges in Emotion Style Transfer: An Exploration with a Lexical Substitution PipelineCode0
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation0
Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning0
Large Scale Multi-Actor Generative Dialog Modeling0
Towards Hate Speech Detection at Large via Deep Generative ModelingCode0
Parallel Corpus Filtering via Pre-trained Language Models0
Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized EncodingCode1
A Mixture of h-1 Heads is Better than h Heads0
DiscreTalk: Text-to-Speech as a Machine Translation Problem0
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance ApproachCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified