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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15801–15850 of 17610 papers

TitleStatusHype
Learning Representations for Detecting Abusive Language—0
Robust parfda Statistical Machine Translation Results—0
Alibaba Submission for WMT18 Quality Estimation Task—0
Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments—0
Improving Neural Language Models with Weight Norm Initialization and Regularization—0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition—0
An Unsupervised System for Parallel Corpus Filtering—0
Disney at IEST 2018: Predicting Emotions using an Ensemble—0
An Empirical Study of Machine Translation for the Shared Task of WMT18—0
Using PPM for Health Related Text Detection—0
UTFPR at WMT 2018: Minimalistic Supervised Corpora Filtering for Machine Translation—0
UBC-NLP at IEST 2018: Learning Implicit Emotion With an Ensemble of Language Models—0
What can we gain from language models for morphological inflection?—0
SParse: Ko University Graph-Based Parsing System for the CoNLL 2018 Shared Task—0
ELMoLex: Connecting ELMo and Lexicon Features for Dependency Parsing—0
Bidirectional Generative Adversarial Networks for Neural Machine Translation—0
Estimating Marginal Probabilities of n-grams for Recurrent Neural Language Models—0
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text GenerationCode0
A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding—0
A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling CheckCode0
Dual Fixed-Size Ordinally Forgetting Encoding (FOFE) for Competitive Neural Language Models—0
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data—0
Decipherment of Substitution Ciphers with Neural Language Models—0
How to represent a word and predict it, too: Improving tied architectures for language modelling—0
Interpretable Emoji Prediction via Label-Wise Attention LSTMs—0
Neural Multitask Learning for Simile Recognition—0
On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling—0
Synthetic Data Made to Order: The Case of ParsingCode0
Session-level Language Modeling for Conversational Speech—0
Put It Back: Entity Typing with Language Model EnhancementCode0
Recovering Missing Characters in Old Hawaiian Writing—0
Quantifying Context Overlap for Training Word Embeddings—0
Learning Recurrent Binary/Ternary WeightsCode0
Adaptive Input Representations for Neural Language ModelingCode1
Countering Language Drift via Grounding—0
Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling—0
A bird's eye view on coherence, and a worm's eye view on cohesion—0
Unsupervised Word Discovery with Segmental Neural Language Models—0
Automatic Data Expansion for Customer-care Spoken Language Understanding—0
Controllable Neural Story Plot Generation via Reward ShapingCode0
Building a Lemmatizer and a Spell-checker for Sorani Kurdish—0
Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis—0
Hindi-English Code-Switching Speech Corpus—0
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!—0
FRAGE: Frequency-Agnostic Word RepresentationCode0
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection—0
Curriculum-Based Neighborhood Sampling For Sequence Prediction—0
Document Informed Neural Autoregressive Topic Models with Distributional PriorCode0
Visual Speech Language Models—0
Automatic, Personalized, and Flexible Playlist Generation using Reinforcement Learning—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