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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16801–16850 of 17610 papers

TitleStatusHype
LSTM Neural Reordering Feature for Statistical Machine Translation—0
Regularizing RNNs by Stabilizing ActivationsCode0
Natural Language Understanding with Distributed RepresentationCode0
Spoken Language Translation for Polish—0
DenseCap: Fully Convolutional Localization Networks for Dense CaptioningCode0
BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large VocabulariesCode0
Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation—0
Improving Neural Machine Translation Models with Monolingual DataCode1
Alternative structures for character-level RNNsCode0
Task Loss Estimation for Sequence PredictionCode0
Generating Sentences from a Continuous SpaceCode1
Recurrent Neural Networks Hardware Implementation on FPGACode0
Learning Articulated Motion Models from Visual and Lingual Signals—0
An Exploration of Softmax Alternatives Belonging to the Spherical Loss FamilyCode0
Oracle performance for visual captioningCode0
Grounding of Textual Phrases in Images by ReconstructionCode0
Larger-Context Language Modelling—0
Visual Language Modeling on CNN Image Representations—0
The Goldilocks Principle: Reading Children's Books with Explicit Memory RepresentationsCode0
Multinomial Loss on Held-out Data for the Sparse Non-negative Matrix Language Model—0
Semi-supervised Sequence LearningCode0
Mining Local Gazetteers of Literary Chinese with CRF and Pattern based Methods for Biographical Information in Chinese History—0
adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs—0
Attention with Intention for a Neural Network Conversation Model—0
A language model based approach towards large scale and lightweight language identification systems—0
Feedforward Sequential Memory Neural Networks without Recurrent Feedback—0
Language Segmentation—0
Parameterized Neural Network Language Models for Information Retrieval—0
SentiCap: Generating Image Descriptions with Sentiments—0
Batch Normalized Recurrent Neural Networks—0
使用詞向量表示與概念資訊於中文大詞彙連續語音辨識之語言模型調適(Exploring Word Embedding and Concept Information for Language Model Adaptation in Mandarin Large Vocabulary Continuous Speech Recognition) [In Chinese]—0
以語言模型判斷學習者文句流暢度(Analyzing Learners `Writing Fluency Based on Language Model)[In Chinese]—0
Acquiring distributed representations for verb-object pairs by using word2vec—0
Construction of Semantic Collocation Bank Based on Semantic Dependency Parsing—0
Distant-supervised Language Model for Detecting Emotional Upsurge on Twitter—0
An Improved Hierarchical Word Sequence Language Model Using Directional Information—0
A Machine Learning Method to Distinguish Machine Translation from Human Translation—0
English to Chinese Translation: How Chinese Character Matters—0
Neural Network Language Model for Chinese Pinyin Input Method Engine—0
Machine Translation Experiments on PADIC: A Parallel Arabic DIalect Corpus—0
Large-scale Dictionary Construction via Pivot-based Statistical Machine Translation with Significance Pruning and Neural Network Features—0
Measuring Popularity of Machine-Generated Sentences Using Term Count, Document Frequency, and Dependency Language Model—0
Toward Algorithmic Discovery of Biographical Information in Local Gazetteers of Ancient China—0
System Combination of RBMT plus SPE and Preordering plus SMT—0
Toshiba MT System Description for the WAT2015 Workshop—0
NAVER Machine Translation System for WAT 2015—0
Real-Time Statistical Speech Translation—0
Noise-Robust ASR for the third 'CHiME' Challenge Exploiting Time-Frequency Masking based Multi-Channel Speech Enhancement and Recurrent Neural Network—0
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge—0
Telugu OCR Framework using Deep Learning—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