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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15601–15650 of 17610 papers

TitleStatusHype
Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition—0
Multilevel Text Normalization with Sequence-to-Sequence Networks and Multisource Learning—0
SciBERT: A Pretrained Language Model for Scientific TextCode1
Language Model Adaptation for Language and Dialect Identification of Text—0
Neural Grammatical Error Correction with Finite State Transducers—0
Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation—0
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL VanishingCode0
Data Augmentation for Rumor Detection Using Context-Sensitive Neural Language Model With Large-Scale Credibility Corpus—0
Pre-trained Language Model Representations for Language Generation—0
Linguistic Knowledge and Transferability of Contextual Representations—0
Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories—0
The emergence of number and syntax units in LSTM language modelsCode0
Evaluating Sequence-to-Sequence Models for Handwritten Text RecognitionCode0
Improving Lemmatization of Non-Standard Languages with Joint LearningCode0
Maybe Deep Neural Networks are the Best Choice for Modeling Source CodeCode0
Partially Shuffling the Training Data to Improve Language ModelsCode0
Neural Language Modeling with Visual Features—0
PROPS: Probabilistic personalization of black-box sequence modelsCode0
Russian Language Datasets in the Digitial Humanities Domain and Their Evaluation with Word EmbeddingsCode0
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies—0
CodeGRU: Context-aware Deep Learning with Gated Recurrent Unit for Source Code ModelingCode0
Efficient Contextual Representation Learning With Continuous Outputs—0
Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications—0
Efficient Contextual Representation Learning Without Softmax Layer—0
Robust Authorship Verification with Transfer Learning—0
Spatio-Temporal Dynamics and Semantic Attribute Enriched Visual Encoding for Video Captioning—0
Zoho at SemEval-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion MiningCode0
Alternating Synthetic and Real Gradients for Neural Language ModelingCode0
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language ModelsCode0
A Framework for Decoding Event-Related Potentials from Text—0
Polyglot Contextual Representations Improve Crosslingual TransferCode0
Using Deep Object Features for Image Descriptions—0
Fixed-Size Ordinally Forgetting Encoding Based Word Sense Disambiguation—0
Enhancing Clinical Concept Extraction with Contextual Embeddings—0
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities—0
Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts—0
Phoneme Level Language Models for Sequence Based Low Resource ASR—0
Emergence of order in random languages—0
A spelling correction model for end-to-end speech recognition—0
Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping—0
TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts—0
A Fully Differentiable Beam Search DecoderCode1
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention—0
Improving Semantic Parsing for Task Oriented Dialog—0
Language Models are Unsupervised Multitask LearnersCode1
Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots—0
BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language ModelCode0
Compression of Recurrent Neural Networks for Efficient Language Modeling—0
On the Choice of Modeling Unit for Sequence-to-Sequence Speech RecognitionCode0
The Referential Reader: A Recurrent Entity Network for Anaphora ResolutionCode0
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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