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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16651–16700 of 17610 papers

TitleStatusHype
Using Term Position Similarity and Language Modeling for Bilingual Document Alignment—0
Using Factored Word Representation in Neural Network Language Models—0
Merged bilingual trees based on Universal Dependencies in Machine Translation—0
ParFDA for Instance Selection for Statistical Machine Translation—0
PJAIT Systems for the WMT 2016—0
The JHU Machine Translation Systems for WMT 2016—0
Recurrent Neural Network based Translation Quality Estimation—0
T\"UB\.ITAK SMT System Submission for WMT2016—0
Normalized Log-Linear Interpolation of Backoff Language Models is Efficient—0
UdS-(retrain|distributional|surface): Improving POS Tagging for OOV Words in German CMC and Web Data—0
The RWTH Aachen University English-Romanian Machine Translation System for WMT 2016—0
JU-USAAR: A Domain Adaptive MT System—0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
Sheffield Systems for the English-Romanian WMT Translation Task—0
IXA Biomedical Translation System at WMT16 Biomedical Translation Task—0
Shallow Discourse Parsing Using Convolutional Neural Network—0
Leveraging Entity Linking and Related Language Projection to Improve Name Transliteration—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
Jointly Learning to Embed and Predict with Multiple Languages—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
Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation—0
Semi-supervised Convolutional Networks for Translation Adaptation with Tiny Amount of In-domain Data—0
The AFRL-MITLL WMT16 News-Translation Task Systems—0
Larger-Context Language Modelling with Recurrent Neural Network—0
Modeling Concept Dependencies in a Scientific Corpus—0
Moses-based official baseline for NEWS 2016—0
N-gram language models for massively parallel devices—0
The TALP--UPC Spanish--English WMT Biomedical Task: Bilingual Embeddings and Char-based Neural Language Model Rescoring in a Phrase-based System—0
Latent Tree Language ModelCode0
Novel Word Embedding and Translation-based Language Modeling for Extractive Speech Summarization—0
Tie-breaker: Using language models to quantify gender bias in sports journalism—0
Recurrent Highway NetworksCode0
Recurrent Memory Array StructuresCode0
Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior Knowledge—0
Predicting and Understanding Law-Making with Word Vectors and an Ensemble Model—0
Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval—0
Using Word Embeddings for Automatic Query Expansion—0
Gender and Interest Targeting for Sponsored Post Advertising at Tumblr—0
NN-grams: Unifying neural network and n-gram language models for Speech Recognition—0
A segmental framework for fully-unsupervised large-vocabulary speech recognitionCode0
On Multiplicative Integration with Recurrent Neural Networks—0
Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015—0
Two Discourse Driven Language Models for Semantics—0
Watch What You Just Said: Image Captioning with Text-Conditional AttentionCode0
Bidirectional Long-Short Term Memory for Video Description—0
Learning to Generate Compositional Color DescriptionsCode0
MuFuRU: The Multi-Function Recurrent Unit—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