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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 17151–17200 of 17610 papers

TitleStatusHype
LIMSI @ WMT'14 Medical Translation Task—0
Free on-line speech recogniser based on Kaldi ASR toolkit producing word posterior lattices—0
Edinburgh's Phrase-based Machine Translation Systems for WMT-14—0
CUNI in WMT14: Chimera Still Awaits Bellerophon—0
DCU-Lingo24 Participation in WMT 2014 Hindi-English Translation task—0
Alex: Bootstrapping a Spoken Dialogue System for a New Domain by Real Users—0
FBK-UPV-UEdin participation in the WMT14 Quality Estimation shared-task—0
Combining Domain Adaptation Approaches for Medical Text Translation—0
Domain Adaptation for Medical Text Translation using Web Resources—0
Anaphora Models and Reordering for Phrase-Based SMT—0
Experiments in Medical Translation Shared Task at WMT 2014—0
Yandex School of Data Analysis Russian-English Machine Translation System for WMT14—0
Unsupervised Adaptation for Statistical Machine Translation—0
Syllable and language model based features for detecting non-scorable tests in spoken language proficiency assessment applications—0
Exploiting Morphological, Grammatical, and Semantic Correlates for Improved Text Difficulty Assessment—0
Building and Evaluating Somali Language Corpora—0
Assessing the Readability of Sentences: Which Corpora and Features?—0
CoNLL 2014 Shared Task: Grammatical Error Correction with a Syntactic N-gram Language Model from a Big Corpora—0
Grammatical error correction using hybrid systems and type filtering—0
Automatic Transliteration of Romanized Dialectal Arabic—0
A Unified Framework for Grammar Error Correction—0
Tuning a Grammar Correction System for Increased Precision—0
POSTECH Grammatical Error Correction System in the CoNLL-2014 Shared Task—0
Towards Temporal Scoping of Relational Facts based on Wikipedia Data—0
Probabilistic Modeling of Joint-context in Distributional Similarity—0
The AMU System in the CoNLL-2014 Shared Task: Grammatical Error Correction by Data-Intensive and Feature-Rich Statistical Machine Translation—0
Predicting Grammaticality on an Ordinal Scale—0
Lattice Desegmentation for Statistical Machine Translation—0
Kneser-Ney Smoothing on Expected CountsCode0
New Directions in Vector Space Models of Meaning—0
Normalizing tweets with edit scripts and recurrent neural embeddings—0
Linguistic Structured Sparsity in Text Categorization—0
Cross-lingual Model Transfer Using Feature Representation Projection—0
Decoder Integration and Expected BLEU Training for Recurrent Neural Network Language Models—0
Biases in Predicting the Human Language Model—0
Bilingually-constrained Phrase Embeddings for Machine Translation—0
Fast and Robust Neural Network Joint Models for Statistical Machine Translation—0
Effective Selection of Translation Model Training Data—0
A Recursive Recurrent Neural Network for Statistical Machine Translation—0
Exponential Reservoir Sampling for Streaming Language Models—0
Improving Lexical Embeddings with Semantic KnowledgeCode0
A Hybrid Approach to Skeleton-based Translation—0
Improved Iterative Correction for Distant Spelling Errors—0
A Provably Correct Learning Algorithm for Latent-Variable PCFGs—0
Faster Phrase-Based Decoding by Refining Feature State—0
Dependency-Based Word Embeddings—0
A Generalized Language Model as the Combination of Skipped n-grams and Modified Kneser Ney SmoothingCode0
Correcting Preposition Errors in Learner English Using Error Case Frames and Feedback Messages—0
Translation Assistance by Translation of L1 Fragments in an L2 Context—0
Unsupervised Morphology-Based Vocabulary Expansion—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