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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 17401–17450 of 17610 papers

TitleStatusHype
Distortion Model Considering Rich Context for Statistical Machine Translation—0
Advancements in Reordering Models for Statistical Machine Translation—0
Polyglot: Distributed Word Representations for Multilingual NLP—0
Syncretism and How to Deal with it in a Morphological Analyzer: a German Example—0
Arabizi Detection and Conversion to Arabic—0
Studying frequency-based approaches to process lexical simplification (Approches \`a base de fr\'equences pour la simplification lexicale) [in French]—0
Taste of Two Different Flavours: Which Manipuri Script works better for English-Manipuri Language pair SMT Systems?—0
Evaluating Unsupervised Language Model Adaptation Methods for Speaking Assessment—0
Hierarchical Alignment Decomposition Labels for Hiero Grammar Rules—0
Discriminating Non-Native English with 350 Words—0
Generating Natural-Language Video Descriptions Using Text-Mined Knowledge—0
Identifying the L1 of non-native writers: the CMU-Haifa system—0
Improving Word Translation Disambiguation by Capturing Multiword Expressions with Dictionaries—0
AI-KU: Using Substitute Vectors and Co-Occurrence Modeling For Word Sense Induction and Disambiguation—0
HLTDI: CL-WSD Using Markov Random Fields for SemEval-2013 Task 10—0
LIMSIILES: Basic English Substitution for Student Answer Assessment at SemEval 2013—0
SRIUBC-Core: Multiword Soft Similarity Models for Textual Similarity—0
SFS-TUE: Compound Paraphrasing with a Language Model and Discriminative Reranking—0
KUL: Data-driven Approach to Temporal Parsing of Newswire Articles—0
Linguistic Regularities in Continuous Space Word Representations—0
Segmentation Strategies for Streaming Speech Translation—0
Semi-Supervised Discriminative Language Modeling with Out-of-Domain Text Data—0
Morphological, Syntactical and Semantic Knowledge in Statistical Machine Translation—0
Knowledge-Rich Morphological Priors for Bayesian Language Models—0
Large-Scale Discriminative Training for Statistical Machine Translation Using Held-Out Line Search—0
Applying Pairwise Ranked Optimisation to Improve the Interpolation of Translation Models—0
Disfluency Detection Using Multi-step Stacked Learning—0
Beyond Left-to-Right: Multiple Decomposition Structures for SMT—0
Dudley North visits North London: Learning When to Transliterate to Arabic—0
Adaptation of Reordering Models for Statistical Machine Translation—0
Improving reordering performance using higher order and structural features—0
A Cross-language Study on Automatic Speech Disfluency Detection—0
Dialectal Arabic to English Machine Translation: Pivoting through Modern Standard Arabic—0
A Systematic Bayesian Treatment of the IBM Alignment Models—0
Grouping Language Model Boundary Words to Speed K--Best Extraction from Hypergraphs—0
Using a Supertagged Dependency Language Model to Select a Good Translation in System Combination—0
Learning Semantic Representations in a Bigram Language Model—0
Joint Space Neural Probabilistic Language Model for Statistical Machine Translation—0
Mac-Morpho Revisited: Towards Robust Part-of-Speech Tagging—0
Modeling Child Divergences from Adult Grammar—0
Incremental Tree Substitution Grammar for Parsing and Sentence Prediction—0
langid.py for better language modelling—0
Open Information Extraction for SOV Language Based on Entity-Predicate Pair Detection—0
Korektor -- A System for Contextual Spell-Checking and Diacritics Completion—0
An Omni-Font Gurmukhi to Shahmukhi Transliteration System—0
Code-Switch Language Model with Inversion Constraints for Mixed Language Speech Recognition—0
Bayesian Language Modelling of German Compounds—0
Approximate Sentence Retrieval for Scalable and Efficient Example-Based Machine Translation—0
Factored Language Model based on Recurrent Neural Network—0
Improved Spelling Error Detection and Correction for Arabic—0
Show:102550
← PrevPage 349 of 353Next →
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