scholarly journals Language Models for the Prediction of SARS-CoV-2 Inhibitors

2021 ◽  
Author(s):  
Andrew E Blanchard ◽  
John Gounley ◽  
Debsindhu Bhowmik ◽  
Mayanka Chandra Shekar ◽  
Isaac Lyngaas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Binding Affinity ◽  
Language Model ◽  
Specific Protein ◽  
Language Models ◽  
Peak Performance ◽  
Protein Targets ◽  
Training Time ◽  
Mixed Precision ◽  
Learning Language

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ~9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

scholarly journals Automated Source Code Generation and Auto-Completion Using Deep Learning: Comparing and Discussing Current Language Model-Related Approaches

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 1-16
Author(s):  
Juan Cruz-Benito ◽  
Sanjay Vishwakarma ◽  
Francisco Martin-Fernandez ◽  
Ismael Faro
Keyword(s):  
Deep Learning ◽  
Learning Community ◽  
Programming Languages ◽  
Language Processing ◽  
Code Generation ◽  
Language Model ◽  
Language Models ◽  
Stochastic Gradient Descent ◽  
Network Architectures ◽  
Learning Architectures

In recent years, the use of deep learning in language models has gained much attention. Some research projects claim that they can generate text that can be interpreted as human writing, enabling new possibilities in many application areas. Among the different areas related to language processing, one of the most notable in applying this type of modeling is programming languages. For years, the machine learning community has been researching this software engineering area, pursuing goals like applying different approaches to auto-complete, generate, fix, or evaluate code programmed by humans. Considering the increasing popularity of the deep learning-enabled language models approach, we found a lack of empirical papers that compare different deep learning architectures to create and use language models based on programming code. This paper compares different neural network architectures like Average Stochastic Gradient Descent (ASGD) Weight-Dropped LSTMs (AWD-LSTMs), AWD-Quasi-Recurrent Neural Networks (QRNNs), and Transformer while using transfer learning and different forms of tokenization to see how they behave in building language models using a Python dataset for code generation and filling mask tasks. Considering the results, we discuss each approach’s different strengths and weaknesses and what gaps we found to evaluate the language models or to apply them in a real programming context.

Astrid

2020 ◽  
Vol 14 (4) ◽  
pp. 471-484
Author(s):  
Suraj Shetiya ◽  
Saravanan Thirumuruganathan ◽  
Nick Koudas ◽  
Gautam Das
Keyword(s):  
Deep Learning ◽  
Objective Function ◽  
Pattern Matching ◽  
Language Processing ◽  
Language Model ◽  
Language Models ◽  
Selectivity Estimation ◽  
Statistical Correlations ◽  
Benchmark Datasets ◽  
Traditional Approaches

Accurate selectivity estimation for string predicates is a long-standing research challenge in databases. Supporting pattern matching on strings (such as prefix, substring, and suffix) makes this problem much more challenging, thereby necessitating a dedicated study. Traditional approaches often build pruned summary data structures such as tries followed by selectivity estimation using statistical correlations. However, this produces insufficiently accurate cardinality estimates resulting in the selection of sub-optimal plans by the query optimizer. Recently proposed deep learning based approaches leverage techniques from natural language processing such as embeddings to encode the strings and use it to train a model. While this is an improvement over traditional approaches, there is a large scope for improvement. We propose Astrid, a framework for string selectivity estimation that synthesizes ideas from traditional and deep learning based approaches. We make two complementary contributions. First, we propose an embedding algorithm that is query-type (prefix, substring, and suffix) and selectivity aware. Consider three strings 'ab', 'abc' and 'abd' whose prefix frequencies are 1000, 800 and 100 respectively. Our approach would ensure that the embedding for 'ab' is closer to 'abc' than 'abd'. Second, we describe how neural language models could be used for selectivity estimation. While they work well for prefix queries, their performance for substring queries is sub-optimal. We modify the objective function of the neural language model so that it could be used for estimating selectivities of pattern matching queries. We also propose a novel and efficient algorithm for optimizing the new objective function. We conduct extensive experiments over benchmark datasets and show that our proposed approaches achieve state-of-the-art results.

scholarly journals COVID-19 sentiment analysis via deep learning during the rise of novel cases

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255615 ◽  
Author(s):  
Rohitash Chandra ◽  
Aswin Krishna
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Language Model ◽  
Deep Understanding ◽  
Language Models ◽  
Catastrophic Events ◽  
Social Scientists ◽  
Significant Group ◽  
Global Vector

Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a catastrophic event that has raised a number of psychological issues such as depression given abrupt social changes and lack of employment. Advancements of deep learning-based language models have been promising for sentiment analysis with data from social networks such as Twitter. Given the situation with COVID-19 pandemic, different countries had different peaks where rise and fall of new cases affected lock-downs which directly affected the economy and employment. During the rise of COVID-19 cases with stricter lock-downs, people have been expressing their sentiments in social media. This can provide a deep understanding of human psychology during catastrophic events. In this paper, we present a framework that employs deep learning-based language models via long short-term memory (LSTM) recurrent neural networks for sentiment analysis during the rise of novel COVID-19 cases in India. The framework features LSTM language model with a global vector embedding and state-of-art BERT language model. We review the sentiments expressed for selective months in 2020 which covers the major peak of novel cases in India. Our framework utilises multi-label sentiment classification where more than one sentiment can be expressed at once. Our results indicate that the majority of the tweets have been positive with high levels of optimism during the rise of the novel COVID-19 cases and the number of tweets significantly lowered towards the peak. We find that the optimistic, annoyed and joking tweets mostly dominate the monthly tweets with much lower portion of negative sentiments. The predictions generally indicate that although the majority have been optimistic, a significant group of population has been annoyed towards the way the pandemic was handled by the authorities.

HIDING CRITICAL INFORMATION WHEN TRAINING LANGUAGE MODELS

2021 ◽  
pp. 15-18
Author(s):  
A. Evtushenko
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Text Processing ◽  
Language Model ◽  
Personal Data ◽  
Language Models ◽  
Training Dataset ◽  
Critical Information ◽  
Research Company ◽  
Learning Language

Machine learning language models are combinations of algorithms and neural networks designed for text processing composed in natural language (Natural Language Processing, NLP).  In 2020, the largest language model from the artificial intelligence research company OpenAI, GPT-3, was released, the maximum number of parameters of which reaches 175 billion. The parameterization of the model increased by more than 100 times made it possible to improve the quality of generated texts to a level that is hard to distinguish from human-written texts. It is noteworthy that this model was trained on a training dataset mainly collected from open sources on the Internet, the volume of which is estimated at 570 GB.  This article discusses the problem of memorizing critical information, in particular, personal data of individual, at the stage of training large language models (GPT-2/3 and derivatives), and also describes an algorithmic approach to solving this problem, which consists in additional preprocessing training dataset and refinement of the model inference in the context of generating pseudo-personal data and embedding into the results of work on the tasks of summarization, text generation, formation of answers to questions and others from the field of seq2seq.

scholarly journals Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2712
Author(s):  
JongYoon Lim ◽  
Inkyu Sa ◽  
Ho Seok Ahn ◽  
Norina Gasteiger ◽  
Sanghyub John Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Auxiliary Information ◽  
Language Models ◽  
Research Fields ◽  
Software Packages ◽  
Public Dataset ◽  
High Degree ◽  
Learning Language ◽  
Important Building Block

Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper.

KM-BERT: A Pre-trained BERT for Korean Medical Natural Language Processing (Preprint)

2021 ◽  
Author(s):  
Yoojoong Kim ◽  
Jeong Moon Lee ◽  
Moon Joung Jang ◽  
Yun Jin Yum ◽  
Jong-Ho Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Pearson Correlation ◽  
Language Model ◽  
Language Models ◽  
Korean Language ◽  
Medical Texts ◽  
Proposed Model

BACKGROUND With advances in deep learning and natural language processing, analyzing medical texts is becoming increasingly important. Nonetheless, a study on medical-specific language models has not yet been conducted given the importance of medical texts. OBJECTIVE Korean medical text is highly difficult to analyze because of the agglutinative characteristics of the language as well as the complex terminologies in the medical domain. To solve this problem, we collected a Korean medical corpus and used it to train language models. METHODS In this paper, we present a Korean medical language model based on deep learning natural language processing. The proposed model was trained using the pre-training framework of BERT for the medical context based on a state-of-the-art Korean language model. RESULTS After pre-training, the proposed method showed increased accuracies of 0.147 and 0.148 for the masked language model with next sentence prediction. In the intrinsic evaluation, the next sentence prediction accuracy improved by 0.258, which is a remarkable enhancement. In addition, the extrinsic evaluation of Korean medical semantic textual similarity data showed a 0.046 increase in the Pearson correlation. CONCLUSIONS The results demonstrated the superiority of the proposed model for Korean medical natural language processing. We expect that our proposed model can be extended for application to various languages and domains.

Using Pre-trained Language Model to Enhance Active Learning for Sentence Matching

2022 ◽  
Vol 21 (2) ◽  
pp. 1-19
Author(s):  
Guirong Bai ◽  
Shizhu He ◽  
Kang Liu ◽  
Jun Zhao
Keyword(s):  
Active Learning ◽  
Language Model ◽  
Experimental Results ◽  
Language Models ◽  
Data Driven ◽  
Learning Approach ◽  
Sentence Matching ◽  
Learning Language

Active learning is an effective method to substantially alleviate the problem of expensive annotation cost for data-driven models. Recently, pre-trained language models have been demonstrated to be powerful for learning language representations. In this article, we demonstrate that the pre-trained language model can also utilize its learned textual characteristics to enrich criteria of active learning. Specifically, we provide extra textual criteria with the pre-trained language model to measure instances, including noise, coverage, and diversity. With these extra textual criteria, we can select more efficient instances for annotation and obtain better results. We conduct experiments on both English and Chinese sentence matching datasets. The experimental results show that the proposed active learning approach can be enhanced by the pre-trained language model and obtain better performance.

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