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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 17401–17450 of 17610 papers

TitleStatusHype
PATHS: A System for Accessing Cultural Heritage Collections—0
NAIST at 2013 CoNLL Grammatical Error Correction Shared Task—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
Dialectal Arabic to English Machine Translation: Pivoting through Modern Standard Arabic—0
A Cross-language Study on Automatic Speech Disfluency Detection—0
HLTDI: CL-WSD Using Markov Random Fields for SemEval-2013 Task 10—0
Improving reordering performance using higher order and structural features—0
Adaptation of Reordering Models for Statistical Machine Translation—0
AI-KU: Using Substitute Vectors and Co-Occurrence Modeling For Word Sense Induction and Disambiguation—0
Applying Pairwise Ranked Optimisation to Improve the Interpolation of Translation Models—0
Identifying the L1 of non-native writers: the CMU-Haifa system—0
Disfluency Detection Using Multi-step Stacked Learning—0
A Systematic Bayesian Treatment of the IBM Alignment Models—0
Evaluating Unsupervised Language Model Adaptation Methods for Speaking Assessment—0
Grouping Language Model Boundary Words to Speed K--Best Extraction from Hypergraphs—0
Hierarchical Alignment Decomposition Labels for Hiero Grammar Rules—0
Generating Natural-Language Video Descriptions Using Text-Mined Knowledge—0
Discriminating Non-Native English with 350 Words—0
Improving Word Translation Disambiguation by Capturing Multiword Expressions with Dictionaries—0
Dudley North visits North London: Learning When to Transliterate to Arabic—0
Beyond Left-to-Right: Multiple Decomposition Structures for SMT—0
Semi-Supervised Discriminative Language Modeling with Out-of-Domain Text Data—0
Taste of Two Different Flavours: Which Manipuri Script works better for English-Manipuri Language pair SMT Systems?—0
LIMSIILES: Basic English Substitution for Student Answer Assessment at SemEval 2013—0
Linguistic Regularities in Continuous Space Word Representations—0
Morphological, Syntactical and Semantic Knowledge in Statistical Machine Translation—0
Studying frequency-based approaches to process lexical simplification (Approches \`a base de fr\'equences pour la simplification lexicale) [in French]—0
SRIUBC-Core: Multiword Soft Similarity Models for Textual Similarity—0
SFS-TUE: Compound Paraphrasing with a Language Model and Discriminative Reranking—0
Segmentation Strategies for Streaming Speech Translation—0
Knowledge-Rich Morphological Priors for Bayesian Language Models—0
KUL: Data-driven Approach to Temporal Parsing of Newswire Articles—0
Large-Scale Discriminative Training for Statistical Machine Translation Using Held-Out Line Search—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
Modeling Child Divergences from Adult Grammar—0
Mac-Morpho Revisited: Towards Robust Part-of-Speech Tagging—0
Incremental Tree Substitution Grammar for Parsing and Sentence Prediction—0
Harvesting Parallel Text in Multiple Languages with Limited Supervision—0
Combining Statistical Translation Techniques for Cross-Language Information Retrieval—0
An Omni-Font Gurmukhi to Shahmukhi Transliteration System—0
Approximate Sentence Retrieval for Scalable and Efficient Example-Based Machine Translation—0
A Conditional Random Field-based Traditional Chinese Base Phrase Parser for SIGHAN Bake-off 2012 Evaluation—0
Conversion between Scripts of Punjabi: Beyond Simple Transliteration—0
Bayesian Language Modelling of German Compounds—0
Factored Language Model based on Recurrent Neural Network—0
Improved Spelling Error Detection and Correction for Arabic—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