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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 17101–17150 of 17610 papers

TitleStatusHype
Incremental Semantic Role Labeling with Tree Adjoining Grammar—0
Fine-Grained Contextual Predictions for Hard Sentiment Words—0
Aligning context-based statistical models of language with brain activity during reading—0
Improve Statistical Machine Translation with Context-Sensitive Bilingual Semantic Embedding Model—0
Go Climb a Dependency Tree and Correct the Grammatical Errors—0
A Neural Network for Factoid Question Answering over Paragraphs—0
Confidence-based Rewriting of Machine Translation Output—0
Translation Rules with Right-Hand Side Lattices—0
Translation Modeling with Bidirectional Recurrent Neural Networks—0
Two Improvements to Left-to-Right Decoding for Hierarchical Phrase-based Machine Translation—0
Recurrent Neural Network RegularizationCode0
An Approach for Text Steganography Based on Markov ChainsCode0
Pushdown Automata in Statistical Machine Translation—0
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNsCode0
Class-Based Language Modeling for Translating into Morphologically Rich Languages—0
Biber Redux: Reconsidering Dimensions of Variation in American English—0
Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild—0
Chinese Word Ordering Errors Detection and Correction for Non-Native Chinese Language Learners—0
Interpolated Dirichlet Class Language Model for Speech Recognition Incorporating Long-distance N-grams—0
From neighborhood to parenthood: the advantages of dependency representation over bigrams in Brown clusteringCode0
A Structured Language Model for Incremental Tree-to-String Translation—0
Recurrent Neural Network-based Tuple Sequence Model for Machine Translation—0
Morfessor FlatCat: An HMM-Based Method for Unsupervised and Semi-Supervised Learning of MorphologyCode0
A PAC-Bayesian Approach to Minimum Perplexity Language Modeling—0
Co-learning of Word Representations and Morpheme Representations—0
Employing Phonetic Speech Recognition for Language and Dialect Specific Search—0
Integrating Dictionaries into an Unsupervised Model for Myanmar Word Segmentation—0
Automatic Identification of Arabic Language Varieties and Dialects in Social Media—0
Improved Sentence-Level Arabic Dialect Classification—0
Collaboratively Constructed Linguistic Resources for Language Variants and their Exploitation in NLP Application -- the case of Tunisian Arabic and the Social Media—0
Exploring Options for Fast Domain Adaptation of Dependency Parsers—0
Morphological Disambiguation and Text Normalization for Southern Quechua Varieties—0
Predicting sense convergence with distributional semantics: an application to the CogaLex 2014 shared task—0
Subsegmental language detection in Celtic language text—0
IUCL: Combining Information Sources for SemEval Task 5—0
SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features—0
UEdin: Translating L1 Phrases in L2 Context using Context-Sensitive SMT—0
AI-KU: Using Co-Occurrence Modeling for Semantic Similarity—0
Assamese-English Bilingual Machine Translation—0
A Tool for the Automatic Insertion of Diacritics in French (Zodiac : Insertion automatique des signes diacritiques du fran ) [in French]—0
Towards End-To-End Speech Recognition with Recurrent Neural Networks—0
The KIT-LIMSI Translation System for WMT 2014—0
Parallel FDA5 for Fast Deployment of Accurate Statistical Machine Translation Systems—0
Phrasal: A Toolkit for New Directions in Statistical Machine Translation—0
Large-scale Exact Decoding: The IMS-TTT submission to WMT14—0
The DCU-ICTCAS MT system at WMT 2014 on German-English Translation Task—0
Stanford University's Submissions to the WMT 2014 Translation Task—0
Target-Centric Features for Translation Quality Estimation—0
Postech's System Description for Medical Text Translation Task—0
The Karlsruhe Institute of Technology Translation Systems for the WMT 2014—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