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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16501–16550 of 17610 papers

TitleStatusHype
Chinese Grammatical Error Diagnosis with Long Short-Term Memory Networks—0
Chinese Grammatical Error Diagnosis Using Single Word Embedding—0
A Proposition-Based Abstractive Summariser—0
Evaluating anaphora and coreference resolution to improve automatic keyphrase extraction—0
Integrating Encyclopedic Knowledge into Neural Language Models—0
Word Midas Powered by StringNet: Discovering Lexicogrammatical Constructions in Situ—0
UQAM-NTL: Named entity recognition in Twitter messages—0
Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection—0
Kyoto-NMT: a Neural Machine Translation implementation in ChainerCode0
Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis—0
Syntactic and Lexical Complexity in Italian Noncanonical Structures—0
Splitting compounds with ngrams—0
Sub-Word Similarity based Search for Embeddings: Inducing Rare-Word Embeddings for Word Similarity Tasks and Language Modelling—0
Succinct Data Structures for NLP-at-Scale—0
Supervised classification of end-of-lines in clinical text with no manual annotation—0
Learning to translate from graded and negative relevance information—0
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities—0
The RWTH Aachen LVCSR system for IWSLT-2016 German Skype conversation recognition task—0
Natural Language Generation through Character-based RNNs with Finite-state Prior Knowledge—0
Japanese Lexical Simplification for Non-Native Speakers—0
Temporal Modelling of Geospatial Words in Twitter—0
Syntactic realization with data-driven neural tree grammarsCode0
Testing the Processing Hypothesis of word order variation using a probabilistic language model—0
Phonotactic Modeling of Extremely Low Resource Languages—0
Recurrent Neural Network with Word Embedding for Complaint Classification—0
LIMSI@IWSLT’16: MT Track—0
Predicting human similarity judgments with distributional models: The value of word associations.—0
Predictive Incremental Parsing Helps Language Modeling—0
The IOIT English ASR system for IWSLT 2016—0
Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation—0
Language and Dialect Discrimination Using Compression-Inspired Language Models—0
Product Review Summarization by Exploiting Phrase Properties—0
QCRI’s Machine Translation Systems for IWSLT’16—0
Mathematical Information Retrieval based on Type Embeddings and Query Expansion—0
Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition—0
Attention-based Memory Selection Recurrent Network for Language Modeling—0
Learning Python Code Suggestion with a Sparse Pointer NetworkCode0
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling—0
A dataset and exploration of models for understanding video data through fill-in-the-blank question-answeringCode0
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition—0
Coherent Dialogue with Attention-based Language Models—0
Visualizing Linguistic Shift—0
Variable Computation in Recurrent Neural Networks—0
What Do Recurrent Neural Network Grammars Learn About Syntax?Code0
Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media TextCode0
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes—0
Gradients of Counterfactuals—0
LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems—0
TopicRNN: A Recurrent Neural Network with Long-Range Semantic DependencyCode0
Neural Architecture Search with Reinforcement LearningCode0
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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