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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 1670116750 of 17610 papers

TitleStatusHype
Sequence-to-Sequence Learning as Beam-Search OptimizationCode0
Gated Word-Character Recurrent Language ModelCode0
Assessing Relative Sentence Complexity using an Incremental CCG Parser0
A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models0
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification0
Grammatical error correction using neural machine translation0
An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation0
Generation from Abstract Meaning Representation using Tree Transducers0
Generalizing and Hybridizing Count-based and Neural Language ModelsCode0
Incorporating Side Information into Recurrent Neural Network Language Models0
Gender-Distinguishing Features in Film Dialogue0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity0
Enriching Cold Start Personalized Language Model Using Social Network Information0
Integrating Morphological Desegmentation into Phrase-based Decoding0
ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter0
Inter-document Contextual Language model0
Interlocking Phrases in Phrase-based Statistical Machine Translation0
Effects of Communicative Pressures on Novice L2 Learners' Use of Optional Formal Devices0
Extraction of Bilingual Technical Terms for Chinese-Japanese Patent Translation0
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks0
Embedding Senses for Efficient Graph-based Word Sense Disambiguation0
PIC a Different Word: A Simple Model for Lexical Substitution in Context0
Towards Semantic-based Hybrid Machine Translation between Bulgarian and English0
Model Combination for Correcting Preposition Selection Errors0
Sentential Paraphrasing as Black-Box Machine Translation0
Transition-Based Syntactic Linearization with Lookahead FeaturesCode0
Temporal Action Detection Using a Statistical Language ModelCode0
TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling0
Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies0
LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles0
Using Related Languages to Enhance Statistical Language Models0
UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports0
UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment ClassificationCode0
UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation0
Deep API Learning0
Automatic Construction of Discourse Corpora for Dialogue Translation0
Syntactically Guided Neural Machine Translation0
Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health0
Noisy Parallel Approximate Decoding for Conditional Recurrent Language Model0
On Improving Informativity and Grammaticality for Multi-Sentence Compression0
LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues0
TheanoLM - An Extensible Toolkit for Neural Network Language Modeling0
Improving Image Captioning by Concept-based Sentence Reranking0
A Corpus of Read and Spontaneous Upper Saxon German Speech for ASR Evaluation0
Cysill Ar-lein: A Corpus of Written Contemporary Welsh Compiled from an On-line Spelling and Grammar Checker0
Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian0
Extracting Weighted Language Lexicons from Wikipedia0
Evaluating a Deterministic Shift-Reduce Neural Parser for Constituent Parsing0
Using SMT for OCR Error Correction of Historical Texts0
Using a Cross-Language Information Retrieval System based on OHSUMED to Evaluate the Moses and KantanMT Statistical Machine Translation Systems0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified