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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 17001–17050 of 17610 papers

TitleStatusHype
Using sub-word n-gram models for dealing with OOV in large vocabulary speech recognition for Latvian—0
Learning to Interpret and Describe Abstract Scenes—0
Deep Learning and Continuous Representations for Natural Language Processing—0
Convolutional Neural Network for Paraphrase IdentificationCode0
I Can Has Cheezburger? A Nonparanormal Approach to Combining Textual and Visual Information for Predicting and Generating Popular Meme Descriptions—0
Fast and Accurate Preordering for SMT using Neural Networks—0
An Incremental Algorithm for Transition-based CCG Parsing—0
Extractive Summarisation Based on Keyword Profile and Language Model—0
Unsupervised Code-Switching for Multilingual Historical Document Transcription—0
Two/Too Simple Adaptations of Word2Vec for Syntax ProblemsCode0
When and why are log-linear models self-normalizing?—0
Lexicon-Free Conversational Speech Recognition with Neural Networks—0
Morphological Word-Embeddings—0
Spinning Straw into Gold: Using Free Text to Train Monolingual Alignment Models for Non-factoid Question Answering—0
Multi-Target Machine Translation with Multi-Synchronous Context-free Grammars—0
Modeling Word Meaning in Context with Substitute Vectors—0
Semantic parsing of speech using grammars learned with weak supervision—0
Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation—0
Leveraging Twitter for Low-Resource Conversational Speech Language Modeling—0
Mining and discovering biographical information in Difangzhi with a language-model-based approach—0
Bengali to Assamese Statistical Machine Translation using Moses (Corpus Based)—0
A Simple Way to Initialize Recurrent Networks of Rectified Linear UnitsCode0
Hierarchical Statistical Semantic Realization for Minimal Recursion Semantics—0
Multilingual Reliability and ``Semantic'' Structure of Continuous Word Spaces—0
End-To-End Memory NetworksCode2
genCNN: A Convolutional Architecture for Word Sequence Prediction—0
Knowledge-based Query Expansion in Real-Time Microblog Search—0
Encoding Source Language with Convolutional Neural Network for Machine Translation—0
Multiple Adjunction in Feature-Based Tree-Adjoining Grammar—0
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks—0
Topic Level Disambiguation for Weak Queries—0
A Linear Dynamical System Model for Text—0
Phrase-based Image Captioning—0
Gated Feedback Recurrent Neural Networks—0
Improving Term Frequency Normalization for Multi-topical Documents, and Application to Language Modeling Approaches—0
Beyond Word-based Language Model in Statistical Machine Translation—0
Scaling Recurrent Neural Network Language Models—0
Phrase Based Language Model for Statistical Machine Translation: Empirical Study—0
Phrase Based Language Model For Statistical Machine Translation—0
A Hybrid Approach to Grapheme-Phoneme Conversion—0
Count-based State Merging for Probabilistic Regular Tree Grammars—0
Modelling and Optimizing on Syntactic N-Grams for Statistical Machine Translation—0
Semantic Parsing of Ambiguous Input through Paraphrasing and Verification—0
Learning Composition Models for Phrase EmbeddingsCode0
Simple Image Description Generator via a Linear Phrase-Based Approach—0
Learning Longer Memory in Recurrent Neural NetworksCode0
Pragmatic Neural Language Modelling in Machine Translation—0
Diverse Embedding Neural Network Language Models—0
Incremental Adaptation Strategies for Neural Network Language Models—0
Video (language) modeling: a baseline for generative models of natural videosCode0
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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