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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14851–14900 of 17610 papers

TitleStatusHype
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data—0
A language score based output selection method for multilingual speech recognition—0
DagoBERT: Generating Derivational Morphology with a Pretrained Language ModelCode0
Improving Neural Language Generation with Spectrum Control—0
GM-RKB WikiText Error Correction Task and BaselinesCode0
Building Language Models for Morphological Rich Low-Resource Languages using Data from Related Donor Languages: the Case of Uyghur—0
Cross-sentence Pre-trained Model for Interactive QA matching—0
Improving the Language Model for Low-Resource ASR with Online Text Corpora—0
Exploring Pre-training with Alignments for RNN Transducer based End-to-End Speech Recognition—0
Embeddings for Named Entity Recognition in Geoscience Portuguese Literature—0
Class-based LSTM Russian Language Model with Linguistic Information—0
Automatic Myanmar Image Captioning using CNN and LSTM-Based Language Model—0
Acoustic-Phonetic Approach for ASR of Less Resourced Languages Using Monolingual and Cross-Lingual Information—0
Evaluating Approaches to Personalizing Language Models—0
Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives—0
IRIT at TRAC 2020—0
Implementation of Supervised Training Approaches for Monolingual Word Sense Alignment: ACDH-CH System Description for the MWSA Shared Task at GlobaLex 2020—0
Adaptation of Deep Bidirectional Transformers for Afrikaans Language—0
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers—0
Aggression Identification in Social Media: a Transfer Learning Based Approach—0
DNN-Based Multilingual Automatic Speech Recognition for Wolaytta using Oromo Speech—0
Evaluating the Impact of Sub-word Information and Cross-lingual Word Embeddings on Mi'kmaq Language Modelling—0
Is Language Modeling Enough? Evaluating Effective Embedding Combinations—0
A Multimodal Educational Corpus of Oral Courses: Annotation, Analysis and Case Study—0
Selecting Informative Contexts Improves Language Model Finetuning—0
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition—0
Machine Translation from Spoken Language to Sign Language using Pre-trained Language Model as Encoder—0
Language Models for Cloze Task Answer Generation in Russian—0
Speech Transcription Challenges for Resource Constrained Indigenous Language Cree—0
Language Modeling with a General Second-Order RNN—0
On the Exploration of English to Urdu Machine Translation—0
Style Variation as a Vantage Point for Code-Switching—0
Stylometry in a Bilingual Setup—0
On Construction of the ASR-oriented Indian English Pronunciation Dictionary—0
Recognizing Semantic Relations by Combining Transformers and Fully Connected Models—0
No Data to Crawl? Monolingual Corpus Creation from PDF Files of Truly low-Resource Languages in Peru—0
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause AttachmentCode0
Neural Models for Predicting Celtic Mutations—0
Minority Positive Sampling for Switching Points - an Anecdote for the Code-Mixing Language Modeling—0
Multi-Task Learning using AraBert for Offensive Language Detection—0
Multi-scale Transformer Language Models—0
The INCOMSLAV Platform: Experimental Website with Integrated Methods for Measuring Linguistic Distances and Asymmetries in Receptive Multilingualism—0
Japanese Realistic Textual Entailment Corpus—0
Jamo Pair Encoding: Subcharacter Representation-based Extreme Korean Vocabulary Compression for Efficient Subword Tokenization—0
Wiki-40B: Multilingual Language Model Dataset—0
Knowledge Injection into Dialogue Generation via Language Models—0
Template Guided Text Generation for Task-Oriented Dialogue—0
Context based Text-generation using LSTM networks—0
EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation—0
Investigating Transferability in Pretrained Language ModelsCode0
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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