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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15851–15900 of 17610 papers

TitleStatusHype
On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling—0
Supervised and Unsupervised Minimalist Quality Estimators: Vicomtech's Participation in the WMT 2018 Quality Estimation Task—0
UTFPR at WMT 2018: Minimalistic Supervised Corpora Filtering for Machine Translation—0
What can we gain from language models for morphological inflection?—0
Using PPM for Health Related Text Detection—0
Zero-Shot Learning Based Approach For Medieval Word Recognition Using Deep-Learned Features—0
A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling CheckCode0
Interpretable Emoji Prediction via Label-Wise Attention LSTMs—0
Bidirectional Generative Adversarial Networks for Neural Machine Translation—0
An Unsupervised System for Parallel Corpus Filtering—0
How to represent a word and predict it, too: Improving tied architectures for language modelling—0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition—0
Improving Neural Language Models with Weight Norm Initialization and Regularization—0
ELMoLex: Connecting ELMo and Lexicon Features for Dependency Parsing—0
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data—0
A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding—0
An Empirical Study of Machine Translation for the Shared Task of WMT18—0
Disney at IEST 2018: Predicting Emotions using an Ensemble—0
Decipherment of Substitution Ciphers with Neural Language Models—0
Dual Fixed-Size Ordinally Forgetting Encoding (FOFE) for Competitive Neural Language Models—0
會議語音辨識使用語者資訊之語言模型調適技術 (On the Use of Speaker-Aware Language Model Adaptation Techniques for Meeting Speech Recognition ) [In Chinese]—0
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text GenerationCode0
Alibaba Submission for WMT18 Quality Estimation Task—0
Estimating Marginal Probabilities of n-grams for Recurrent Neural Language Models—0
Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments—0
Learning Recurrent Binary/Ternary WeightsCode0
Automatic Data Expansion for Customer-care Spoken Language Understanding—0
Building a Lemmatizer and a Spell-checker for Sorani Kurdish—0
Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling—0
Countering Language Drift via Grounding—0
A bird's eye view on coherence, and a worm's eye view on cohesion—0
Controllable Neural Story Plot Generation via Reward ShapingCode0
Unsupervised Word Discovery with Segmental Neural Language Models—0
Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis—0
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!—0
Hindi-English Code-Switching Speech Corpus—0
FRAGE: Frequency-Agnostic Word RepresentationCode0
Curriculum-Based Neighborhood Sampling For Sequence Prediction—0
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection—0
Document Informed Neural Autoregressive Topic Models with Distributional PriorCode0
Visual Speech Language Models—0
Automatic, Personalized, and Flexible Playlist Generation using Reinforcement Learning—0
Knowledge-Aware Conversational Semantic Parsing Over Web Tables—0
Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet—0
Context-Free Transductions with Neural StacksCode0
Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency—0
RNNs as psycholinguistic subjects: Syntactic state and grammatical dependencyCode0
Random Language Model—0
t-Exponential Memory Networks for Question-Answering Machines—0
Chittron: An Automatic Bangla Image Captioning System—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