scholarly journals An Attentive Fourier-Augmented Image-Captioning Transformer

2021 ◽  
Vol 11 (18) ◽  
pp. 8354
Author(s):  
Raymond Ian Osolo ◽  
Zhan Yang ◽  
Jun Long
Keyword(s):  
Fourier Transformation ◽  
Fourier Transforms ◽  
State Of The Art ◽  
Language Model ◽  
Image Representation ◽  
Image Features ◽  
Language Models ◽  
Object Location ◽  
Image Captioning ◽  
Image Object

Many vision–language models that output natural language, such as image-captioning models, usually use image features merely for grounding the captions and most of the good performance of the model can be attributed to the language model, which does all the heavy lifting, a phenomenon that has persisted even with the emergence of transformer-based architectures as the preferred base architecture of recent state-of-the-art vision–language models. In this paper, we make the images matter more by using fast Fourier transforms to further breakdown the input features and extract more of their intrinsic salient information, resulting in more detailed yet concise captions. This is achieved by performing a 1D Fourier transformation on the image features first in the hidden dimension and then in the sequence dimension. These extracted features alongside the region proposal image features result in a richer image representation that can then be queried to produce the associated captions, which showcase a deeper understanding of image–object–location relationships than similar models. Extensive experiments performed on the MSCOCO dataset demonstrate a CIDER-D, BLEU-1, and BLEU-4 score of 130, 80.5, and 39, respectively, on the MSCOCO benchmark dataset.

scholarly journals An Analysis of the Use of Feed-Forward Sub-Modules for Transformer-Based Image Captioning Tasks

2021 ◽  
Vol 11 (24) ◽  
pp. 11635
Author(s):  
Raymond Ian Osolo ◽  
Zhan Yang ◽  
Jun Long
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Current Transformer ◽  
Image Features ◽  
Language Models ◽  
Image Captioning ◽  
Feed Forward ◽  
Sequence Generation ◽  
Current State ◽  
Batch Sizes

In the quest to make deep learning systems more capable, a number of more complex, more computationally expensive and memory intensive algorithms have been proposed. This switchover glosses over the capabilities of many of the simpler systems or modules within them to adequately address current and future problems. This has led to some of the deep learning research being inaccessible to researchers who don’t possess top-of-the-line hardware. The use of simple feed forward networks has not been explicitly explored in the current transformer-based vision-language field. In this paper, we use a series of feed-forward layers to encode image features, and caption embeddings, alleviating some of the effects of the computational complexities that accompany the use of the self-attention mechanism and limit its application in long sequence task scenarios. We demonstrate that a decoder does not require masking for conditional short sequence generation where the task is not only dependent on the previously generated sequence, but another input such as image features. We perform an empirical and qualitative analysis of the use of linear transforms in place of self-attention layers in vision-language models, and obtain competitive results on the MSCOCO dataset. Our best feed-forward model obtains average scores of over 90% of the current state-of-the-art pre-trained Oscar model in the conventional image captioning metrics. We also demonstrate that the proposed models take less time training and use less memory at larger batch sizes and longer sequence lengths.

scholarly journals MSA Transformer

2021 ◽  
Author(s):  
Roshan Rao ◽  
Jason Liu ◽  
Robert Verkuil ◽  
Joshua Meier ◽  
John F. Canny ◽  
...  
Keyword(s):  
Structure Learning ◽  
State Of The Art ◽  
Language Model ◽  
Language Modeling ◽  
Language Models ◽  
Multiple Sequence ◽  
Wide Margin ◽  
Current State ◽  
Individual Sequences ◽  
And Function

AbstractUnsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins. Protein language models studied to date have been trained to perform inference from individual sequences. The longstanding approach in computational biology has been to make inferences from a family of evolutionarily related sequences by fitting a model to each family independently. In this work we combine the two paradigms. We introduce a protein language model which takes as input a set of sequences in the form of a multiple sequence alignment. The model interleaves row and column attention across the input sequences and is trained with a variant of the masked language modeling objective across many protein families. The performance of the model surpasses current state-of-the-art unsupervised structure learning methods by a wide margin, with far greater parameter efficiency than prior state-of-the-art protein language models.

scholarly journals Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees

2016 ◽  
Vol 4 ◽  
pp. 477-490 ◽  
Author(s):  
Ehsan Shareghi ◽  
Matthias Petri ◽  
Gholamreza Haffari ◽  
Trevor Cohn
Keyword(s):  
Infinite Order ◽  
State Of The Art ◽  
Language Model ◽  
Language Models ◽  
Memory Usage ◽  
Suffix Trees ◽  
Construction Time ◽  
Modest Increase ◽  
Order Language ◽  
Language Modelling

Efficient methods for storing and querying are critical for scaling high-order m-gram language models to large corpora. We propose a language model based on compressed suffix trees, a representation that is highly compact and can be easily held in memory, while supporting queries needed in computing language model probabilities on-the-fly. We present several optimisations which improve query runtimes up to 2500×, despite only incurring a modest increase in construction time and memory usage. For large corpora and high Markov orders, our method is highly competitive with the state-of-the-art KenLM package. It imposes much lower memory requirements, often by orders of magnitude, and has runtimes that are either similar (for training) or comparable (for querying).

scholarly journals 3M: Multi-style image caption generation using Multi-modality features under Multi-UPDOWN model

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Chengxi Li ◽  
Brent Harrison
Keyword(s):  
Qualitative Study ◽  
State Of The Art ◽  
Image Features ◽  
Generative Model ◽  
Image Captioning ◽  
Text Features ◽  
Image Caption Generation ◽  
Image Caption

In this paper, we build a multi-style generative model for stylish image captioning which uses multi-modality image features, ResNeXt features, and text features generated by DenseCap. We propose the 3M model, a Multi-UPDOWN caption model that encodes multi-modality features and decodes them into captions. We demonstrate the effectiveness of our model on generating human-like captions by examining its performance on two datasets, the PERSONALITY-CAPTIONS dataset, and the FlickrStyle10K dataset. We compare against a variety of state-of-the-art baselines on various automatic NLP metrics such as BLEU, ROUGE-L, CIDEr, SPICE, etc \footnote{code will be available at https://github.com/cici-ai-club/3M}. A qualitative study has also been done to verify our 3M model can be used for generating different stylized captions.

scholarly journals Text Augmentation Using BERT for Image Captioning

2020 ◽  
Vol 10 (17) ◽  
pp. 5978
Author(s):  
Viktar Atliha ◽  
Dmitrij Šešok
Keyword(s):  
Neural Networks ◽  
Human Computer Interaction ◽  
State Of The Art ◽  
Language Model ◽  
Research Field ◽  
The State ◽  
Training Dataset ◽  
Image Description ◽  
Image Captioning ◽  
Training Set

Image captioning is an important task for improving human-computer interaction as well as for a deeper understanding of the mechanisms underlying the image description by human. In recent years, this research field has rapidly developed and a number of impressive results have been achieved. The typical models are based on a neural networks, including convolutional ones for encoding images and recurrent ones for decoding them into text. More than that, attention mechanism and transformers are actively used for boosting performance. However, even the best models have a limit in their quality with a lack of data. In order to generate a variety of descriptions of objects in different situations you need a large training set. The current commonly used datasets although rather large in terms of number of images are quite small in terms of the number of different captions per one image. We expanded the training dataset using text augmentation methods. Methods include augmentation with synonyms as a baseline and the state-of-the-art language model called Bidirectional Encoder Representations from Transformers (BERT). As a result, models that were trained on a datasets augmented show better results than that models trained on a dataset without augmentation.

scholarly journals When Low Resource NLP Meets Unsupervised Language Model: Meta-Pretraining then Meta-Learning for Few-Shot Text Classification (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13773-13774
Author(s):  
Shumin Deng ◽  
Ningyu Zhang ◽  
Zhanlin Sun ◽  
Jiaoyan Chen ◽  
Huajun Chen
Keyword(s):  
Text Classification ◽  
State Of The Art ◽  
Language Model ◽  
Language Models ◽  
Generic Model ◽  
Effective Strategy ◽  
Linguistic Features ◽  
Meta Learning ◽  
Promising Solution ◽  
Model Initialization

Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes. In such challenging scenarios, recent studies have often used meta-learning to simulate the few-shot task, thus negating implicit common linguistic features across tasks. This paper addresses such problems using meta-learning and unsupervised language models. Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks. We show that our approach is not only simple but also produces a state-of-the-art performance on a well-studied sentiment classification dataset. It can thus be further suggested that pretraining could be a promising solution for few-shot learning of many other NLP tasks. The code and the dataset to replicate the experiments are made available at https://github.com/zxlzr/FewShotNLP.

Backward and trigger-based language models for statistical machine translation

2013 ◽  
Vol 21 (2) ◽  
pp. 201-226 ◽  
Author(s):  
DEYI XIONG ◽  
MIN ZHANG
Keyword(s):  
Mutual Information ◽  
Machine Translation ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Experimental Results ◽  
Language Models ◽  
Knowledge Sources ◽  
Long Distance ◽  
Translation Quality

AbstractThe language model is one of the most important knowledge sources for statistical machine translation. In this article, we present two extensions to standard n-gram language models in statistical machine translation: a backward language model that augments the conventional forward language model, and a mutual information trigger model which captures long-distance dependencies that go beyond the scope of standard n-gram language models. We introduce algorithms to integrate the two proposed models into two kinds of state-of-the-art phrase-based decoders. Our experimental results on Chinese/Spanish/Vietnamese-to-English show that both models are able to significantly improve translation quality in terms of BLEU and METEOR over a competitive baseline.

scholarly journals CloudLM: a Cloud-based Language Model for Machine Translation

2016 ◽  
Vol 105 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Jorge Ferrández-Tordera ◽  
Sergio Ortiz-Rojas ◽  
Antonio Toral
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
State Of The Art ◽  
Language Model ◽  
Essential Element ◽  
Language Models ◽  
Language Modelling ◽  
Statistical Approaches

Abstract Language models (LMs) are an essential element in statistical approaches to natural language processing for tasks such as speech recognition and machine translation (MT). The advent of big data leads to the availability of massive amounts of data to build LMs, and in fact, for the most prominent languages, using current techniques and hardware, it is not feasible to train LMs with all the data available nowadays. At the same time, it has been shown that the more data is used for a LM the better the performance, e.g. for MT, without any indication yet of reaching a plateau. This paper presents CloudLM, an open-source cloud-based LM intended for MT, which allows to query distributed LMs. CloudLM relies on Apache Solr and provides the functionality of state-of-the-art language modelling (it builds upon KenLM), while allowing to query massive LMs (as the use of local memory is drastically reduced), at the expense of slower decoding speed.

scholarly journals Where do Clinical Language Models Break Down? A Critical Behavioural Exploration of the ClinicalBERT Deep Transformer Model

2021 ◽  
Vol 6 (1) ◽  
pp. 1-4
Author(s):  
Alexander MacLean ◽  
Alexander Wong
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Language Model ◽  
Language Models ◽  
Clinical Knowledge ◽  
Language Understanding ◽  
Improved Performance ◽  
Transformer Model ◽  
Clinical Domain

The introduction of Bidirectional Encoder Representations from Transformers (BERT) was a major breakthrough for transfer learning in natural language processing, enabling state-of-the-art performance across a large variety of complex language understanding tasks. In the realm of clinical language modeling, the advent of BERT led to the creation of ClinicalBERT, a state-of-the-art deep transformer model pretrained on a wealth of patient clinical notes to facilitate for downstream predictive tasks in the clinical domain. While ClinicalBERT has been widely leveraged by the research community as the foundation for building clinical domain-specific predictive models given its overall improved performance in the Medical Natural Language inference (MedNLI) challenge compared to the seminal BERT model, the fine-grained behaviour and intricacies of this popular clinical language model has not been well-studied. Without this deeper understanding, it is very challenging to understand where ClinicalBERT does well given its additional exposure to clinical knowledge, where it doesn't, and where it can be improved in a meaningful manner. Motivated to garner a deeper understanding, this study presents a critical behaviour exploration of the ClinicalBERT deep transformer model using MedNLI challenge dataset to better understanding the following intricacies: 1) decision-making similarities between ClinicalBERT and BERT (leverage a new metric we introduce called Model Alignment), 2) where ClinicalBERT holds advantages over BERT given its clinical knowledge exposure, and 3) where ClinicalBERT struggles when compared to BERT. The insights gained about the behaviour of ClinicalBERT will help guide towards new directions for designing and training clinical language models in a way that not only addresses the remaining gaps and facilitates for further improvements in clinical language understanding performance, but also highlights the limitation and boundaries of use for such models.

scholarly journals Gated POS-Level Language Model for Authorship Verification

2020 ◽  
Author(s):  
Linshu Ouyang ◽  
Yongzheng Zhang ◽  
Hui Liu ◽  
Yige Chen ◽  
Yipeng Wang
Keyword(s):  
State Of The Art ◽  
Language Model ◽  
Training Data ◽  
Language Models ◽  
Effective Parameters ◽  
Part Of Speech ◽  
Authorship Verification ◽  
Verification Methods ◽  
Optimal Accuracy ◽  
Pos Tagger

Authorship verification is an important problem that has many applications. The state-of-the-art deep authorship verification methods typically leverage character-level language models to encode author-specific writing styles. However, they often fail to capture syntactic level patterns, leading to sub-optimal accuracy in cross-topic scenarios. Also, due to imperfect cross-author parameter sharing, it's difficult for them to distinguish author-specific writing style from common patterns, leading to data-inefficient learning. This paper introduces a novel POS-level (Part of Speech) gated RNN based language model to effectively learn the author-specific syntactic styles. The author-agnostic syntactic information obtained from the POS tagger pre-trained on large external datasets greatly reduces the number of effective parameters of our model, enabling the model to learn accurate author-specific syntactic styles with limited training data. We also utilize a gated architecture to learn the common syntactic writing styles with a small set of shared parameters and let the author-specific parameters focus on each author's special syntactic styles. Extensive experimental results show that our method achieves significantly better accuracy than state-of-the-art competing methods, especially in cross-topic scenarios (over 5\% in terms of AUC-ROC).

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