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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
Combining Statistical Translation Techniques for Cross-Language Information Retrieval—0
Harvesting Parallel Text in Multiple Languages with Limited Supervision—0
Conversion between Scripts of Punjabi: Beyond Simple Transliteration—0
Readability Classification for German using Lexical, Syntactic, and Morphological Features—0
Is Bad Structure Better Than No Structure?: Unsupervised Parsing for Realisation Ranking—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
Quantifying Semantics using Complex Network Analysis—0
Neural Probabilistic Language Model for System Combination—0
Statistical Input Method based on a Phrase Class n-gram Model—0
Using Collocations and K-means Clustering to Improve the N-pos Model for Japanese IME—0
A Conditional Random Field-based Traditional Chinese Base Phrase Parser for SIGHAN Bake-off 2012 Evaluation—0
A Language Modeling Approach to Identifying Code-Switched Sentences and Words—0
Applying Statistical Post-Editing to English-to-Korean Rule-based Machine Translation System—0
Introduction of a Probabilistic Language Model to Non-Factoid Question Answering Using Example Q\&A Pairs—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
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
Using Domain-specific and Collaborative Resources for Term Translation—0
A Finite-State Approach to Phrase-Based Statistical Machine Translation—0
Assigning Deep Lexical Types Using Structured Classifier Features for Grammatical Dependencies—0
A Methodology for Obtaining Concept Graphs from Word Graphs—0
Effect of Language and Error Models on Efficiency of Finite-State Spell-Checking and Correction—0
Exact Sampling and Decoding in High-Order Hidden Markov Models—0
A Systematic Comparison of Phrase Table Pruning Techniques—0
Exploring Adaptor Grammars for Native Language Identification—0
A Discriminative Model for Query Spelling Correction with Latent Structural SVM—0
A Comparison of Vector-based Representations for Semantic Composition—0
Document-Wide Decoding for Phrase-Based Statistical Machine Translation—0
Entropy-based Pruning for Phrase-based Machine Translation—0
Cross-Lingual Language Modeling with Syntactic Reordering for Low-Resource Speech Recognition—0
Translation Model Based Cross-Lingual Language Model Adaptation: from Word Models to Phrase Models—0
N-gram-based Tense Models for Statistical Machine Translation—0
Language Model Rest Costs and Space-Efficient Storage—0
Polarity Inducing Latent Semantic Analysis—0
Left-to-Right Tree-to-String Decoding with Prediction—0
EMNLP@CPH: Is frequency all there is to simplicity?—0
Detecting Text Reuse with Modified and Weighted N-grams—0
Modelling selectional preferences in a lexical hierarchy—0
The OpenGrm open-source finite-state grammar software libraries—0
NiuTrans: An Open Source Toolkit for Phrase-based and Syntax-based Machine TranslationCode0
Discovering Factions in the Computational Linguistics Community—0
An Exploration of Forest-to-String Translation: Does Translation Help or Hurt Parsing?—0
Akamon: An Open Source Toolkit for Tree/Forest-Based Statistical Machine Translation—0
Applying Collocation Segmentation to the ACL Anthology Reference Corpus—0
Deep Learning for NLP (without Magic)—0
A Class-Based Agreement Model for Generating Accurately Inflected Translations—0
Fast and Scalable Decoding with Language Model Look-Ahead for Phrase-based Statistical Machine Translation—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