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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1405114100 of 17610 papers

TitleStatusHype
Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question AnsweringCode1
Language Model is All You Need: Natural Language Understanding as Question Answering0
Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources0
A Neuro-Symbolic Method for Solving Differential and Functional Equations0
Indic-Transformers: An Analysis of Transformer Language Models for Indian LanguagesCode0
Improving RNN transducer with normalized jointer network0
CharBERT: Character-aware Pre-trained Language ModelCode1
Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition0
Conditioned Text Generation with Transfer for Closed-Domain Dialogue SystemsCode1
Data-to-Text Generation with Iterative Text EditingCode1
Modeling Event Salience in Narratives via Barthes' Cardinal Functions0
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuningCode1
Sound Natural: Content Rephrasing in Dialog SystemsCode0
On the Sentence Embeddings from Pre-trained Language ModelsCode1
Improving Variational Autoencoder for Text Modelling with Timestep-Wise RegularisationCode0
IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP0
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution0
ABNIRML: Analyzing the Behavior of Neural IR ModelsCode1
Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating0
Filtering Noisy Parallel Corpus using Transformers with Proxy Task LearningCode1
Cross-Lingual Transformers for Neural Automatic Post-Editing0
GTCOM Neural Machine Translation Systems for WMT200
Extended Study on Using Pretrained Language Models and YiSi-1 for Machine Translation Evaluation0
Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT0
Improving Parallel Data Identification using Iteratively Refined Sentence Alignments and Bilingual Mappings of Pre-trained Language Models0
A3-108 Machine Translation System for Similar Language Translation Shared Task 20200
Biomedical Event Extraction as Multi-turn Question AnsweringCode0
The University of Edinburgh’s English-Tamil and English-Inuktitut Submissions to the WMT20 News Translation Task0
SJTU-NICT’s Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task0
NJU’s submission to the WMT20 QE Shared Task0
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
Low-Resource Translation as Language Modeling0
NICT Kyoto Submission for the WMT’20 Quality Estimation Task: Intermediate Training for Domain and Task Adaptation0
Naver Labs Europe’s Participation in the Robustness, Chat, and Biomedical Tasks at WMT 20200
Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation0
Grammaticality and Language Modelling0
NLP-PINGAN-TECH @ CL-SciSumm 20200
Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model0
Monash-Summ@LongSumm 20 SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline0
Log-Linear Reformulation of the Noisy Channel Model for Document-Level Neural Machine Translation0
Controlling the Imprint of Passivization and Negation in Contextualized RepresentationsCode0
Detecting Entailment in Code-Mixed Hindi-English ConversationsCode0
HLTRI at W-NUT 2020 Shared Task-3: COVID-19 Event Extraction from Twitter Using Multi-Task Hopfield Pooling0
CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets - RoBERTa Ensembles and The Continued Relevance of Handcrafted Features0
ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents0
imec-ETRO-VUB at W-NUT 2020 Shared Task-3: A multilabel BERT-based system for predicting COVID-19 events0
Intelligent Analyses on Storytelling for Impact Measurement0
Truecasing German user-generated conversational text0
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling0
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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