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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16601–16650 of 17610 papers

TitleStatusHype
An Analysis of the Ability of Statistical Language Models to Capture the Structural Properties of Language—0
Enabling text readability awareness during the micro planning phase of NLG applications—0
Generating Paraphrases from DBPedia using Deep Learning—0
LVCSR System on a Hybrid GPU-CPU Embedded Platform for Real-Time Dialog Applications—0
Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions—0
Which techniques does your application use?: An information extraction framework for scientific articles—0
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC—0
Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix TreesCode0
Numerically Grounded Language Models for Semantic Error Correction—0
Generative Knowledge Transfer for Neural Language Models—0
A deep language model for software codeCode0
Babler - Data Collection from the Web to Support Speech Recognition and Keyword Search—0
English-Portuguese Biomedical Translation Task Using a Genuine Phrase-Based Statistical Machine Translation Approach—0
Distributed representation and estimation of WFST-based n-gram models—0
Argumentative texts and clause types—0
A Linear Baseline Classifier for Cross-Lingual Pronoun Prediction—0
Edinburgh's Statistical Machine Translation Systems for WMT16—0
Abu-MaTran at WMT 2016 Translation Task: Deep Learning, Morphological Segmentation and Tuning on Character Sequences—0
Cross-lingual Pronoun Prediction for English, French and German with Maximum Entropy Classification—0
Cross-lingual Pronoun Prediction with Linguistically Informed Features—0
CobaltF: A Fluent Metric for MT Evaluation—0
Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks—0
DFKI's system for WMT16 IT-domain task, including analysis of systematic errors—0
Hybrid Morphological Segmentation for Phrase-Based Machine Translation—0
WMT 2016 Multimodal Translation System Description based on Bidirectional Recurrent Neural Networks with Double-Embeddings—0
Using Factored Word Representation in Neural Network Language Models—0
Using Term Position Similarity and Language Modeling for Bilingual Document Alignment—0
UdS-(retrain|distributional|surface): Improving POS Tagging for OOV Words in German CMC and Web Data—0
Pronoun Prediction with Linguistic Features and Example Weighing—0
Pronoun Prediction with Latent Anaphora Resolution—0
Pronoun Language Model and Grammatical Heuristics for Aiding Pronoun Prediction—0
JU-USAAR: A Domain Adaptive MT System—0
Leveraging Entity Linking and Related Language Projection to Improve Name Transliteration—0
ParFDA for Instance Selection for Statistical Machine Translation—0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
Sheffield Systems for the English-Romanian WMT Translation Task—0
Moses-based official baseline for NEWS 2016—0
T\"UB\.ITAK SMT System Submission for WMT2016—0
The TALP--UPC Spanish--English WMT Biomedical Task: Bilingual Embeddings and Char-based Neural Language Model Rescoring in a Phrase-based System—0
PJAIT Systems for the WMT 2016—0
The JHU Machine Translation Systems for WMT 2016—0
Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation—0
KSAnswer: Question-answering System of Kangwon National University and Sogang University in the 2016 BioASQ Challenge—0
Phrase-Based SMT for Finnish with More Data, Better Models and Alternative Alignment and Translation Tools—0
Recurrent Neural Network based Translation Quality Estimation—0
It-disambiguation and source-aware language models for cross-lingual pronoun prediction—0
The AFRL-MITLL WMT16 News-Translation Task Systems—0
IXA Biomedical Translation System at WMT16 Biomedical Translation Task—0
Merged bilingual trees based on Universal Dependencies in Machine Translation—0
The RWTH Aachen University English-Romanian Machine Translation System for WMT 2016—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