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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 17451–17500 of 17610 papers

TitleStatusHype
Is Bad Structure Better Than No Structure?: Unsupervised Parsing for Realisation Ranking—0
A Language Modeling Approach to Identifying Code-Switched Sentences and Words—0
Code-Switch Language Model with Inversion Constraints for Mixed Language Speech Recognition—0
Neural Probabilistic Language Model for System Combination—0
Statistical Input Method based on a Phrase Class n-gram Model—0
Readability Classification for German using Lexical, Syntactic, and Morphological Features—0
Quantifying Semantics using Complex Network Analysis—0
langid.py for better language modelling—0
Open Information Extraction for SOV Language Based on Entity-Predicate Pair Detection—0
Lattice Rescoring for Speech Recognition using Large Scale Distributed Language Models—0
Statistical Method of Building Dialect Language Models for ASR Systems—0
Language Modeling for Spoken Dialogue System based on Filtering using Predicate-Argument Structures—0
Korektor -- A System for Contextual Spell-Checking and Diacritics Completion—0
Using Collocations and K-means Clustering to Improve the N-pos Model for Japanese IME—0
Introduction of a Probabilistic Language Model to Non-Factoid Question Answering Using Example Q\&A Pairs—0
Applying Statistical Post-Editing to English-to-Korean Rule-based Machine Translation System—0
遞迴式類神經網路語言模型應用額外資訊於語音辨識之研究 (Recurrent Neural Network-based Language Modeling with Extra Information Cues for Speech Recognition) [In Chinese]—0
Translating Collocation using Monolingual and Parallel Corpus—0
Large-Scale Syntactic Language Modeling with Treelets—0
N-gram-based Tense Models for Statistical Machine Translation—0
Topic Models for Dynamic Translation Model Adaptation—0
Mixing Multiple Translation Models in Statistical Machine Translation—0
Left-to-Right Tree-to-String Decoding with Prediction—0
Modelling selectional preferences in a lexical hierarchy—0
Language Model Rest Costs and Space-Efficient Storage—0
Text Segmentation by Language Using Minimum Description Length—0
Polarity Inducing Latent Semantic Analysis—0
Translation Model Based Cross-Lingual Language Model Adaptation: from Word Models to Phrase Models—0
Translation Model Adaptation for Statistical Machine Translation with Monolingual Topic Information—0
The OpenGrm open-source finite-state grammar software libraries—0
NiuTrans: An Open Source Toolkit for Phrase-based and Syntax-based Machine TranslationCode0
Word Sense Disambiguation Improves Information Retrieval—0
Using Domain-specific and Collaborative Resources for Term Translation—0
WSD for n-best reranking and local language modeling in SMT—0
WFST-Based Grapheme-to-Phoneme Conversion: Open Source tools for Alignment, Model-Building and Decoding—0
Unsupervised Semantic Role Induction with Global Role Ordering—0
Utilizing Dependency Language Models for Graph-based Dependency Parsing Models—0
Applying Collocation Segmentation to the ACL Anthology Reference Corpus—0
Discovering Factions in the Computational Linguistics Community—0
Combining Word-Level and Character-Level Models for Machine Translation Between Closely-Related Languages—0
Cross-Lingual Language Modeling with Syntactic Reordering for Low-Resource Speech Recognition—0
Deep Learning for NLP (without Magic)—0
Fast Syntactic Analysis for Statistical Language Modeling via Substructure Sharing and Uptraining—0
A Class-Based Agreement Model for Generating Accurately Inflected Translations—0
EMNLP@CPH: Is frequency all there is to simplicity?—0
A Systematic Comparison of Phrase Table Pruning Techniques—0
Exploring Adaptor Grammars for Native Language Identification—0
A Finite-State Approach to Phrase-Based Statistical Machine Translation—0
Entropy-based Pruning for Phrase-based Machine Translation—0
Detecting Text Reuse with Modified and Weighted N-grams—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