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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

TitleStatusHype
CoNLL 2014 Shared Task: Grammatical Error Correction with a Syntactic N-gram Language Model from a Big Corpora—0
Bilingually-constrained Phrase Embeddings for Machine Translation—0
Correcting Preposition Errors in Learner English Using Error Case Frames and Feedback Messages—0
Edinburgh's Phrase-based Machine Translation Systems for WMT-14—0
Grammatical error correction using hybrid systems and type filtering—0
Cross-lingual Model Transfer Using Feature Representation Projection—0
Improving Lexical Embeddings with Semantic KnowledgeCode0
Automatic Transliteration of Romanized Dialectal Arabic—0
Building and Evaluating Somali Language Corpora—0
Alex: Bootstrapping a Spoken Dialogue System for a New Domain by Real Users—0
A Hybrid Approach to Skeleton-based Translation—0
Free on-line speech recogniser based on Kaldi ASR toolkit producing word posterior lattices—0
Decoder Integration and Expected BLEU Training for Recurrent Neural Network Language Models—0
A Provably Correct Learning Algorithm for Latent-Variable PCFGs—0
FBK-UPV-UEdin participation in the WMT14 Quality Estimation shared-task—0
Fast and Robust Neural Network Joint Models for Statistical Machine Translation—0
Biases in Predicting the Human Language Model—0
Dependency-Based Word Embeddings—0
A Generalized Language Model as the Combination of Skipped n-grams and Modified Kneser Ney SmoothingCode0
A Unified Framework for Grammar Error Correction—0
Faster Phrase-Based Decoding by Refining Feature State—0
Effective Selection of Translation Model Training Data—0
Domain Adaptation for Medical Text Translation using Web Resources—0
A Recursive Recurrent Neural Network for Statistical Machine Translation—0
Combining Domain Adaptation Approaches for Medical Text Translation—0
DCU-Lingo24 Participation in WMT 2014 Hindi-English Translation task—0
New Directions in Vector Space Models of Meaning—0
The DCU-ICTCAS MT system at WMT 2014 on German-English Translation Task—0
POSTECH Grammatical Error Correction System in the CoNLL-2014 Shared Task—0
The Karlsruhe Institute of Technology Translation Systems for the WMT 2014—0
The AMU System in the CoNLL-2014 Shared Task: Grammatical Error Correction by Data-Intensive and Feature-Rich Statistical Machine Translation—0
Postech's System Description for Medical Text Translation Task—0
Translation Assistance by Translation of L1 Fragments in an L2 Context—0
Predicting Grammaticality on an Ordinal Scale—0
The KIT-LIMSI Translation System for WMT 2014—0
Kneser-Ney Smoothing on Expected CountsCode0
Target-Centric Features for Translation Quality Estimation—0
Syllable and language model based features for detecting non-scorable tests in spoken language proficiency assessment applications—0
Large-scale Exact Decoding: The IMS-TTT submission to WMT14—0
Probabilistic Modeling of Joint-context in Distributional Similarity—0
Lattice Desegmentation for Statistical Machine Translation—0
LIMSI @ WMT'14 Medical Translation Task—0
Normalizing tweets with edit scripts and recurrent neural embeddings—0
Towards End-To-End Speech Recognition with Recurrent Neural Networks—0
Stanford University's Submissions to the WMT 2014 Translation Task—0
Parallel FDA5 for Fast Deployment of Accurate Statistical Machine Translation Systems—0
Tuning a Grammar Correction System for Increased Precision—0
Towards Temporal Scoping of Relational Facts based on Wikipedia Data—0
Phrasal: A Toolkit for New Directions in Statistical Machine Translation—0
Linguistic Structured Sparsity in Text Categorization—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