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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1445114500 of 17610 papers

TitleStatusHype
FENAS: Flexible and Expressive Neural Architecture Search0
BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models0
Extended Study on Using Pretrained Language Models and YiSi-1 for Machine Translation Evaluation0
Cross-Lingual Transformers for Neural Automatic Post-Editing0
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering0
Detecting Entailment in Code-Mixed Hindi-English ConversationsCode0
Centering-based Neural Coherence Modeling with Hierarchical Discourse Segments0
GTCOM Neural Machine Translation Systems for WMT200
From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers0
BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition0
Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating0
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling0
Improving Parallel Data Identification using Iteratively Refined Sentence Alignments and Bilingual Mappings of Pre-trained Language Models0
Analysing Word Representation from the Input and Output Embeddings in Neural Network Language ModelsCode0
Evaluation of Transfer Learning for Adverse Drug Event (ADE) and Medication Entity Extraction0
Intelligent Analyses on Storytelling for Impact Measurement0
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking0
Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection0
LIMIT-BERT : Linguistics Informed Multi-Task BERTCode0
POSTECH-ETRI’s Submission to the WMT2020 APE Shared Task: Automatic Post-Editing with Cross-lingual Language Model0
Machine Translation Reference-less Evaluation using YiSi-2 with Bilingual Mappings of Massive Multilingual Language Model0
NJU’s submission to the WMT20 QE Shared Task0
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling0
PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation0
SunBear at WNUT-2020 Task 2: Improving BERT-Based Noisy Text Classification with Knowledge of the Data domain0
The University of Edinburgh’s English-Tamil and English-Inuktitut Submissions to the WMT20 News Translation Task0
Looking inside Noun Compounds: Unsupervised Prepositional and Free Paraphrasing0
Low-Resource Translation as Language Modeling0
Monash-Summ@LongSumm 20 SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline0
Truecasing German user-generated conversational text0
Naver Labs Europe’s Participation in the Robustness, Chat, and Biomedical Tasks at WMT 20200
TESA: A Task in Entity Semantic Aggregation for Abstractive Summarization0
Tri-Train: Automatic Pre-Fine Tuning between Pre-Training and Fine-Tuning for SciNER0
The Amazing World of Neural Language Generation0
SJTU-NICT’s Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task0
NLP-PINGAN-TECH @ CL-SciSumm 20200
Revisiting Representation Degeneration Problem in Language Modeling0
Personal Information Leakage Detection in ConversationsCode0
Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages0
Learning Physical Common Sense as Knowledge Graph Completion via BERT Data Augmentation and Constrained Tucker Factorization0
Log-Linear Reformulation of the Noisy Channel Model for Document-Level Neural Machine Translation0
Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation0
Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model0
NICT Kyoto Submission for the WMT’20 Quality Estimation Task: Intermediate Training for Domain and Task Adaptation0
Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach0
SLM: Learning a Discourse Language Representation with Sentence Unshuffling0
Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition0
Semantic Labeling Using a Deep Contextualized Language ModelCode0
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation0
Seq2Mol: Automatic design of de novo molecules conditioned by the target protein sequences through deep neural networks0
Show:102550
← PrevPage 290 of 353Next →

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