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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16701–16750 of 17610 papers

TitleStatusHype
LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles—0
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification—0
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 Twitter—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
Effects of Communicative Pressures on Novice L2 Learners' Use of Optional Formal Devices—0
Assessing Relative Sentence Complexity using an Incremental CCG Parser—0
A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models—0
An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation—0
Generation from Abstract Meaning Representation using Tree Transducers—0
Inter-document Contextual Language model—0
Interlocking Phrases in Phrase-based Statistical Machine Translation—0
Incorporating Side Information into Recurrent Neural Network Language Models—0
Extraction of Bilingual Technical Terms for Chinese-Japanese Patent Translation—0
Integrating Morphological Desegmentation into Phrase-based Decoding—0
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks—0
Grammatical error correction using neural machine translation—0
Using Related Languages to Enhance Statistical Language Models—0
Transition-Based Syntactic Linearization with Lookahead FeaturesCode0
Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies—0
PIC a Different Word: A Simple Model for Lexical Substitution in Context—0
Sentential Paraphrasing as Black-Box Machine Translation—0
Generalizing and Hybridizing Count-based and Neural Language ModelsCode0
Deep API Learning—0
Automatic Construction of Discourse Corpora for Dialogue Translation—0
Syntactically Guided Neural Machine Translation—0
Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health—0
Noisy Parallel Approximate Decoding for Conditional Recurrent Language Model—0
On Improving Informativity and Grammaticality for Multi-Sentence Compression—0
LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues—0
Improving Image Captioning by Concept-based Sentence Reranking—0
TheanoLM - An Extensible Toolkit for Neural Network Language Modeling—0
The SI TEDx-UM speech database: a new Slovenian Spoken Language Resource—0
A Corpus of Read and Spontaneous Upper Saxon German Speech for ASR Evaluation—0
Extracting Weighted Language Lexicons from Wikipedia—0
Cysill Ar-lein: A Corpus of Written Contemporary Welsh Compiled from an On-line Spelling and Grammar Checker—0
MoBiL: A Hybrid Feature Set for Automatic Human Translation Quality Assessment—0
SCALE: A Scalable Language Engineering ToolkitCode0
LanguageCrawl: A Generic Tool for Building Language Models Upon Common-Crawl—0
Using SMT for OCR Error Correction of Historical Texts—0
Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality—0
Using a Cross-Language Information Retrieval System based on OHSUMED to Evaluate the Moses and KantanMT Statistical Machine Translation Systems—0
Evaluating a Deterministic Shift-Reduce Neural Parser for Constituent Parsing—0
Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian—0
Linguistically Inspired Language Model Augmentation for MT—0
Syntax-based Multi-system Machine Translation—0
Higher Order Recurrent Neural Networks—0
The Z-loss: a shift and scale invariant classification loss belonging to the Spherical FamilyCode0
Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance—0
Word Ordering Without SyntaxCode0
The IBM 2016 English Conversational Telephone Speech Recognition 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