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Journal articles on the topic 'Text-To-Text Generation'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

V, Pavithra, Rosy S, Srinishanthini R. B, and Prinslin L. "Text-To-Image Generation Using AI." International Journal of Research Publication and Reviews 4, no. 4 (2023): 4932–37. http://dx.doi.org/10.55248/gengpi.234.4.38568.

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Chaddha, Mahima, Sneha Kashid, and Snehal Bhosale Prof Radha Deoghare. "Deep Learning for X-ray Image to Text Generation." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (2019): 1679–82. http://dx.doi.org/10.31142/ijtsrd23168.

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Avhad, Pranjali. "WordCanvas: Text-to-Image Generation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32152.

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This project investigates the novel use of stable dif- fusion techniques to generate high-quality images from detailed text descriptions. The combination of natural language under- standing and computer vision in text-to-image conversion opens up new possibilities for content creation and communication. Using cutting-edge stable diffusion models, our project builds a solid foundation for the generation process, which includes tokenization, pre-processing, specialized architecture design, and post-processing techniques. The advantages include eye-catching images, increased user engagement, cont

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He, Yuze, Yushi Bai, Matthieu Lin, et al. "Text-image conditioned diffusion for consistent text-to-3D generation." Computer Aided Geometric Design 111 (June 2024): 102292. http://dx.doi.org/10.1016/j.cagd.2024.102292.

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Yang, Sen, and Yang Liu. "Data-to-text Generation via Planning." Journal of Physics: Conference Series 1827, no. 1 (2021): 012190. http://dx.doi.org/10.1088/1742-6596/1827/1/012190.

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Teng, Zhaopu. "Abstractive summarization of COVID-19 with transfer text-to-text transformer." Applied and Computational Engineering 2, no. 1 (2023): 232–38. http://dx.doi.org/10.54254/2755-2721/2/20220520.

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As a classic problem of Natural Language Processing, summarization provides convenience for studies, research, and daily life. The performance of generation summarization by Natural Language Processing techniques has attracted considerable attention. Meanwhile, COVID-19, a global explosion event, has led to the emergence of a large number of articles and research. The wide variety of articles makes it a perfect realization object for summarization generation tasks. This paper designed and implemented experiments by fine tuning T5 model to get an abstract summarization of COVID-19 literatures.

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Liu, Fangyu, Qianchu Liu, Shruthi Bannur, et al. "Compositional Zero-Shot Domain Transfer with Text-to-Text Models." Transactions of the Association for Computational Linguistics 11 (2023): 1097–113. http://dx.doi.org/10.1162/tacl_a_00585.

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Abstract Label scarcity is a bottleneck for improving task performance in specialized domains. We propose a novel compositional transfer learning framework (DoT51) for zero-shot domain transfer. Without access to in-domain labels, DoT5 jointly learns domain knowledge (from masked language modelling of unlabelled in-domain free text) and task knowledge (from task training on more readily available general-domain data) in a multi-task manner. To improve the transferability of task training, we design a strategy named NLGU: We simultaneously train natural language generation (NLG) for in-domain l

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Chary, Podakanti Satyajith. "Text Generation: Using Markov Model & LSTM Networks to Generate Realistic Text." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 1323–27. http://dx.doi.org/10.22214/ijraset.2023.57601.

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Abstract: Text generation plays a crucial role in various natural language processing applications, ranging from creative writing to chatbots. This research delves into the realm of text generation by exploring and comparing two distinct techniques: Markov models and Long Short-Term Memory (LSTM) networks. The study focuses on their ability to generate realistic text within specific styles or genres, providing valuable insights into their respective strengths and limitations. Markov models, rooted in probability theory, and LSTM networks, a type of recurrent neural network, represent contrasti

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Liu, Jingyi. "How to Imagine the World with Text? From Text-to-image Generation View." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 644–50. http://dx.doi.org/10.54097/hset.v39i.6619.

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Words are an effective and convenient way to describe the world, but sometimes what the texts convey may be misunderstood by readers. The expression of pictures is more vivid, easy to understand and has no borders, but creating a painting often takes a long time. Text-to-image makes the two expressions complement each other: It makes every ordinary person a “painter”, so that they can feel the world, express themselves, and create more whimsy through many rich pictures. For this vision, technologists are trying their best to improve image generation models, which enables computers to generate

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Chomphooyod, Peerawat, Atiwong Suchato, Nuengwong Tuaycharoen, and Proadpran Punyabukkana. "English grammar multiple-choice question generation using Text-to-Text Transfer Transformer." Computers and Education: Artificial Intelligence 5 (2023): 100158. http://dx.doi.org/10.1016/j.caeai.2023.100158.

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Gong, Heng, Xiaocheng Feng, and Bing Qin. "DiffuD2T: Empowering Data-to-Text Generation with Diffusion." Electronics 12, no. 9 (2023): 2136. http://dx.doi.org/10.3390/electronics12092136.

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Surrounded by structured data, such as medical data, financial data, knowledge bases, etc., data-to-text generation has become an important natural language processing task that can help people better understand the meaning of those data by providing them with user-friendly text. Existing methods for data-to-text generation show promising results in tackling two major challenges: content planning and surface realization, which transform structured data into fluent text. However, they lack an iterative refinement process for generating text, which can enable the model to perfect the text step-b

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Puduppully, Ratish, Yao Fu, and Mirella Lapata. "Data-to-text Generation with Variational Sequential Planning." Transactions of the Association for Computational Linguistics 10 (2022): 697–715. http://dx.doi.org/10.1162/tacl_a_00484.

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Abstract We consider the task of data-to-text generation, which aims to create textual output from non-linguistic input. We focus on generating long-form text, that is, documents with multiple paragraphs, and propose a neural model enhanced with a planning component responsible for organizing high-level information in a coherent and meaningful way. We infer latent plans sequentially with a structured variational model, while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Experiments on two da

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Konarieva, I., D. Pydorenko, and O. Turuta. "A SURVEY OF METHODS OF TEXT-TO-IMAGE TRANSLATION." Bionics of Intelligence 2, no. 93 (2019): 64–68. http://dx.doi.org/10.30837/bi.2019.2(93).11.

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The given work considers the existing methods of text compression (finding keywords or creating summary) using RAKE, Lex Rank, Luhn, LSA, Text Rank algorithms; image generation; text-to-image and image-to-image translation including GANs (generative adversarial networks). Different types of GANs were described such as StyleGAN, GauGAN, Pix2Pix, CycleGAN, BigGAN, AttnGAN. This work aims to show ways to create illustrations for the text. First, key information should be obtained from the text. Second, this key information should be transformed into images. There were proposed several ways to tra

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Wang, Tianming, Xiaojun Wan, and Hanqi Jin. "AMR-To-Text Generation with Graph Transformer." Transactions of the Association for Computational Linguistics 8 (July 2020): 19–33. http://dx.doi.org/10.1162/tacl_a_00297.

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Abstract meaning representation (AMR)-to-text generation is the challenging task of generating natural language texts from AMR graphs, where nodes represent concepts and edges denote relations. The current state-of-the-art methods use graph-to-sequence models; however, they still cannot significantly outperform the previous sequence-to-sequence models or statistical approaches. In this paper, we propose a novel graph-to-sequence model (Graph Transformer) to address this task. The model directly encodes the AMR graphs and learns the node representations. A pairwise interaction function is used

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Li, Yi. "Text-to-Classic: A Diffusion Method for Classical Art Generation Based on Text." Frontiers in Computing and Intelligent Systems 3, no. 1 (2023): 85–89. http://dx.doi.org/10.54097/fcis.v3i1.6030.

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Text-to-Image generation has recently become a hot research topic and diffusion models have achieved remarkable performance in this task. However, most previous researches aim at real scene generation. Few researches focus on classical art paintings. Besides, diffusion models are commonly heavy-weighted with a large number of parameters, which has a high computational cost. In this paper, we aim to solve the classical art paintings synthesis subtask. We propose a lightweight diffusion model Text-to-Classic(T2C) to synthesize classical art paintings according to text descriptions. Experiment re

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Puduppully, Ratish, and Mirella Lapata. "Data-to-text Generation with Macro Planning." Transactions of the Association for Computational Linguistics 9 (2021): 510–27. http://dx.doi.org/10.1162/tacl_a_00381.

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Abstract Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text that is fluent (but often imprecise) and perform quite poorly at selecting appropriate content and ordering it coherently. To overcome some of these issues, we propose a neural model with a macro planning stage followed by a generation stage reminiscent of traditional methods which embrace separate modules for planning and surface realization. Macro plans represent high level organization of important content such as entities, event

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Liu, An-An, Zefang Sun, Ning Xu, et al. "Prior knowledge guided text to image generation." Pattern Recognition Letters 177 (January 2024): 89–95. http://dx.doi.org/10.1016/j.patrec.2023.12.003.

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Han, Huasong, Ziqing Li, Fei Fang, Fei Luo, and Chunxia Xiao. "Text to video generation via knowledge distillation." Metaverse 5, no. 1 (2024): 2425. http://dx.doi.org/10.54517/m.v5i1.2425.

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<p>Text-to-video generation (T2V) has recently attracted more attention due to the wide application scenarios of video media. However, compared with the substantial advances in text-to-image generation (T2I), the research on T2V remains in its early stage. The difficulty mainly lies in maintaining the text-visual semantic consistency and the video temporal coherence. In this paper, we propose a novel distillation and translation GAN (DTGAN) to address these problems. First, we leverage knowledge distillation to guarantee semantic consistency. We distill text-visual mappings from a well-p

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Liao, Jiayi, Xu Chen, Qiang Fu, et al. "Text-to-Image Generation for Abstract Concepts." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3360–68. http://dx.doi.org/10.1609/aaai.v38i4.28122.

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Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the expression of concrete concepts. Unlike concrete concepts that are usually directly associated with physical objects, expressing abstract concepts through natural language requires considerable effort since they are characterized by intricate semantics and connotations. An alternative approach is to leverage images to convey rich visual information as a supplement. Nevertheless, existing Text-to-Image (T2I) models are prima

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Wang, Zeyu, Xiaoyu Liang, and Cheng Wang. "Controllable Text-to-Image Generation with Enhanced Text Encoder and Edge-Preserving Embedding." Journal of Physics: Conference Series 1856, no. 1 (2021): 012003. http://dx.doi.org/10.1088/1742-6596/1856/1/012003.

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Dandekar, Anushree, Rohini Malladi, Payal Gore, and Dr Vipul Dalal. "Text to Image Synthesis using Generative Adversarial Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2723–30. http://dx.doi.org/10.22214/ijraset.2023.50584.

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Abstract: Image generation has been a significant field of research in computer vision and machine learning for several years. It involves generating new images that resemble real-world images based on a given input or set of inputs. This process has a wide range of applications, including video games, computer graphics, and image editing. With the advancements in deep learning, the development of generative models has revolutionized the field of image generation. Generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) have demonstrated remarkable s

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Chao Wang, Chao Wang, Wei Luo Chao Wang, Jia-Rui Zhu Wei Luo, Ying-Chun Xia Jia-Rui Zhu, Jin He Ying-Chun Xia, and Li-Chuan Gu Jin He. "End-to-end Visual Grounding Based on Query Text Guidance and Multi-stage Reasoning." 電腦學刊 35, no. 1 (2024): 083–95. http://dx.doi.org/10.53106/199115992024023501006.

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<p>Visual grounding locates target objects or areas in the image based on natural language expression. Most current methods extract visual features and text embeddings independently, and then carry out complex fusion reasoning to locate target objects mentioned in the query text. However, such independently extracted visual features often contain many features that are irrelevant to the query text or misleading, thus affecting the subsequent multimodal fusion module, and deteriorating target localization. This study introduces a combined network model based on the transformer architectur

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R, Prof Seema. "STABLE DIFFUSION TEXT TO IMAGE USING AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33350.

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The Stable Diffusion Text-to-Image Generation Project is an innovative endeavor in the field of generative adversarial networks (GANs) and natural language processing (NLP). This project aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework to generate highly realistic and coherent images from text prompts. The project leverages recent advancements in deep learning techniques to tackle the challenging task of text-to image synthesis. The project introduces an innovative approach at the crossroads of generative adver

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Mathew, Sibi. "An Overview of Text to Visual Generation Using GAN." Indian Journal of Image Processing and Recognition 4, no. 3 (2024): 1–9. http://dx.doi.org/10.54105/ijipr.a8041.04030424.

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Text-to-visual generation was once a cumbersome task until the advent of deep learning networks. With the introduction of deep learning, both images and videos can now be generated from textual descriptions. Deep learning networks have revolutionized various fields, including computer vision and natural language processing, with the emergence of Generative Adversarial Networks (GANs). GANs have played a significant role in advancing these domains. A GAN typically comprises multiple deep networks combined with other machine learning techniques. In the context of text-to-visual generation, GANs

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Golait, Dr Snehal. "Implementation of Text to Image using Diffusion Model." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34583.

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Text-to-image generation is a transformative field in artificial intelligence, aiming to bridge the semantic gap between textual descriptions and visual representations. This presents a comprehensive approach to tackle this challenging task. Leveraging the advancements in deep learning, natural language processing (NLP), and computer vision, this proposes a cutting-edge model for generating high-fidelity images from textual prompts. Trained on a vast and varied dataset of written descriptions and related images, this model combines an image decoder and a text encoder within a hierarchical fram

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Yashaswini, S., and S. S. Shylaja. "Metrics for Automatic Evaluation of Text from NLP Models for Text to Scene Generation." European Journal of Electrical Engineering and Computer Science 5, no. 4 (2021): 20–25. http://dx.doi.org/10.24018/ejece.2021.5.4.341.

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Performance metrics give us an indication of which model is better for which task. Researchers attempt to apply machine learning and deep learning models to measure the performance of models through cost function or evaluation criteria like Mean square error (MSE) for regression, accuracy, and f1-score for classification tasks Whereas in NLP performance measurement is a complex due variation of ground truth and results obta.

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Tan, Zhaorui, Xi Yang, Zihan Ye, et al. "Semantic Similarity Distance: Towards better text-image consistency metric in text-to-image generation." Pattern Recognition 144 (December 2023): 109883. http://dx.doi.org/10.1016/j.patcog.2023.109883.

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Zhao, Jianyu, Zhiqiang Zhan, Tong Li, et al. "Generative adversarial network for Table-to-Text generation." Neurocomputing 452 (September 2021): 28–36. http://dx.doi.org/10.1016/j.neucom.2021.04.036.

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Konstas, I., and M. Lapata. "A Global Model for Concept-to-Text Generation." Journal of Artificial Intelligence Research 48 (October 30, 2013): 305–46. http://dx.doi.org/10.1613/jair.4025.

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Concept-to-text generation refers to the task of automatically producing textual output from non-linguistic input. We present a joint model that captures content selection ("what to say") and surface realization ("how to say") in an unsupervised domain-independent fashion. Rather than breaking up the generation process into a sequence of local decisions, we define a probabilistic context-free grammar that globally describes the inherent structure of the input (a corpus of database records and text describing some of them). We recast generation as the task of finding the best derivation tree fo

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Liu, Jinglin, Zhiying Zhu, Yi Ren, et al. "Parallel and High-Fidelity Text-to-Lip Generation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1738–46. http://dx.doi.org/10.1609/aaai.v36i2.20066.

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As a key component of talking face generation, lip movements generation determines the naturalness and coherence of the generated talking face video. Prior literature mainly focuses on speech-to-lip generation while there is a paucity in text-to-lip (T2L) generation. T2L is a challenging task and existing end-to-end works depend on the attention mechanism and autoregressive (AR) decoding manner. However, the AR decoding manner generates current lip frame conditioned on frames generated previously, which inherently hinders the inference speed, and also has a detrimental effect on the quality of

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Zhang, Dell, Jiahao Yuan, Xiaoling Wang, and Adam Foster. "Probabilistic Verb Selection for Data-to-Text Generation." Transactions of the Association for Computational Linguistics 6 (December 2018): 511–27. http://dx.doi.org/10.1162/tacl_a_00038.

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In data-to-text Natural Language Generation (NLG) systems, computers need to find the right words to describe phenomena seen in the data. This paper focuses on the problem of choosing appropriate verbs to express the direction and magnitude of a percentage change (e.g., in stock prices). Rather than simply using the same verbs again and again, we present a principled data-driven approach to this problem based on Shannon’s noisy-channel model so as to bring variation and naturalness into the generated text. Our experiments on three large-scale real-world news corpora demonstrate that the propos

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Kring, Brunhild. "Generation Text Goes to College: A Developmental Perspective." Group 36, no. 3 (2012): 233–42. http://dx.doi.org/10.1353/grp.2012.0007.

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Zhou, Yuan, Peng Wang, Lei Xiang, and Haofeng Zhang. "Feature-Grounded Single-Stage Text-to-Image Generation." Tsinghua Science and Technology 29, no. 2 (2024): 469–80. http://dx.doi.org/10.26599/tst.2023.9010023.

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Sun, Bowen, Yamin Li, Jun Zhang, Honghong Xu, Xiaoqiang Ma, and Ping Xia. "Topic Controlled Steganography via Graph-to-Text Generation." Computer Modeling in Engineering & Sciences 136, no. 1 (2023): 157–76. http://dx.doi.org/10.32604/cmes.2023.025082.

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Gomathi, Harsh, and Shubham Bhatt. "SIGN LANGUAGE DETECTION AND TEXT TO SPEECH GENERATION." International Journal of Engineering Applied Sciences and Technology 08, no. 07 (2023): 51–55. http://dx.doi.org/10.33564/ijeast.2023.v08i07.009.

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A vital technique for bridging the communication gap between hearing-impaired and normal individuals is sign language. But given the diversity of today's approximately 7000 sign languages, which vary in hand shapes, body part positions, and motion positions, automated sign language recognition (ASLR) is a challenging method. Researchers are looking into more effective ways to build ASLR systems to find intelligent solutions in order to get around this complexity, and they have shown impressive results. This purpose of this work is to examine the literature on intelligent systems for sign langu

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Zhang, Lingjun, Xinyuan Chen, Yaohui Wang, Yue Lu, and Yu Qiao. "Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7215–23. http://dx.doi.org/10.1609/aaai.v38i7.28550.

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Recently, diffusion-based image generation methods are credited for their remarkable text-to-image generation capabilities, while still facing challenges in accurately generating multilingual scene text images. To tackle this problem, we propose Diff-Text, which is a training-free scene text generation framework for any language. Our model outputs a photo-realistic image given a text of any language along with a textual description of a scene. The model leverages rendered sketch images as priors, thus arousing the potential multilingual-generation ability of the pre-trained Stable Diffusion. B

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Seo, Minseok, Daehan Kim, and Dong-Geol Choi. "Adversarial Shade Generation and Training Text Recognition Algorithm that is Robust to Text in Brightness." Journal of Korea Robotics Society 16, no. 3 (2021): 276–82. http://dx.doi.org/10.7746/jkros.2021.16.3.276.

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Hwang, Myeong-Ha, Jikang Shin, Hojin Seo, Jeong-Seon Im, Hee Cho, and Chun-Kwon Lee. "Ensemble-NQG-T5: Ensemble Neural Question Generation Model Based on Text-to-Text Transfer Transformer." Applied Sciences 13, no. 2 (2023): 903. http://dx.doi.org/10.3390/app13020903.

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Deep learning chatbot research and development is exploding recently to offer customers in numerous industries personalized services. However, human resources are used to create a learning dataset for a deep learning chatbot. In order to augment this, the idea of neural question generation (NQG) has evolved, although it has restrictions on how questions can be expressed in different ways and has a finite capacity for question generation. In this paper, we propose an ensemble-type NQG model based on the text-to-text transfer transformer (T5). Through the proposed model, the number of generated

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Gong, Heng, Xiaocheng Feng, and Bing Qin. "Quality Control for Distantly-Supervised Data-to-Text Generation via Meta Learning." Applied Sciences 13, no. 9 (2023): 5573. http://dx.doi.org/10.3390/app13095573.

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Data-to-text generation plays an important role in natural language processing by processing structured data and helping people understand those data by generating user-friendly descriptive text. It can be applied to news generation, financial report generation, customer service, etc. However, in practice, it needs to adapt to different domains that may lack an annotated training corpus. To alleviate this dataset scarcity problem, distantly-supervised data-to-text generation has emerged, which constructs a training corpus automatically and is more practical to apply to new domains when well-al

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Oneață, Dan, Beáta Lőrincz, Adriana Stan, and Horia Cucu. "FlexLip: A Controllable Text-to-Lip System." Sensors 22, no. 11 (2022): 4104. http://dx.doi.org/10.3390/s22114104.

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The task of converting text input into video content is becoming an important topic for synthetic media generation. Several methods have been proposed with some of them reaching close-to-natural performances in constrained tasks. In this paper, we tackle a subissue of the text-to-video generation problem, by converting the text into lip landmarks. However, we do this using a modular, controllable system architecture and evaluate each of its individual components. Our system, entitled FlexLip, is split into two separate modules: text-to-speech and speech-to-lip, both having underlying controlla

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Zakraoui, Jezia, Moutaz Saleh, Somaya Al-Maadeed, and Jihad Mohammed Jaam. "Improving text-to-image generation with object layout guidance." Multimedia Tools and Applications 80, no. 18 (2021): 27423–43. http://dx.doi.org/10.1007/s11042-021-11038-0.

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AbstractThe automatic generation of realistic images directly from a story text is a very challenging problem, as it cannot be addressed using a single image generation approach due mainly to the semantic complexity of the story text constituents. In this work, we propose a new approach that decomposes the task of story visualization into three phases: semantic text understanding, object layout prediction, and image generation and refinement. We start by simplifying the text using a scene graph triple notation that encodes semantic relationships between the story objects. We then introduce an

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Puduppully, Ratish, Li Dong, and Mirella Lapata. "Data-to-Text Generation with Content Selection and Planning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6908–15. http://dx.doi.org/10.1609/aaai.v33i01.33016908.

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Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order. In this work, we present a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training. We decompose the generation task into two stages. Given a corpus of data records (paired with descriptive documents), we first generate a content plan highlighting which information should be mentioned and in which order and then generate the document

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Feng, Xiaocheng, Heng Gong, Yuyu Chen, et al. "Learning number reasoning for numerical table-to-text generation." International Journal of Machine Learning and Cybernetics 12, no. 8 (2021): 2269–80. http://dx.doi.org/10.1007/s13042-021-01305-9.

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Ma, Da, Xingyu Chen, Ruisheng Cao, Zhi Chen, Lu Chen, and Kai Yu. "Relation-Aware Graph Transformer for SQL-to-Text Generation." Applied Sciences 12, no. 1 (2021): 369. http://dx.doi.org/10.3390/app12010369.

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Generating natural language descriptions for structured representation (e.g., a graph) is an important yet challenging task. In this work, we focus on SQL-to-text, a task that maps a SQL query into the corresponding natural language question. Previous work represents SQL as a sparse graph and utilizes a graph-to-sequence model to generate questions, where each node can only communicate with k-hop nodes. Such a model will degenerate when adapted to more complex SQL queries due to the inability to capture long-term and the lack of SQL-specific relations. To tackle this problem, we propose a rela

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Sarah Senk. "Attention to the Text: Delay and the “ADD Generation”." Transformations: The Journal of Inclusive Scholarship and Pedagogy 25, no. 2 (2016): 78. http://dx.doi.org/10.5325/trajincschped.25.2.0078.

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Senk, Sarah. "Attention to the Text: Delay and the “ADD Generation”." Transformations: The Journal of Inclusive Scholarship and Pedagogy 25, no. 2 (2014): 78–95. http://dx.doi.org/10.1353/tnf.2014.0022.

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Gkatzia, Dimitra, Oliver Lemon, and Verena Rieser. "Data-to-Text Generation Improves Decision-Making Under Uncertainty." IEEE Computational Intelligence Magazine 12, no. 3 (2017): 10–17. http://dx.doi.org/10.1109/mci.2017.2708998.

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Do, Tu Trong, and Tomio Takara. "Vietnamese Text-To-Speech system with precise tone generation." Acoustical Science and Technology 25, no. 5 (2004): 347–53. http://dx.doi.org/10.1250/ast.25.347.

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Srivastava, Ananya. "Advancements in Text-to-Image Generation through Generative AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33569.

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Text-to-image generation, a fascinating intersection of natural language processing and computer vision, has witnessed remarkable progress in recent years. This research paper provides a comprehensive review of the state-of-the-art techniques, challenges, and applications in the field of text-to-image generation. The paper aims to analyze various approaches, discuss their strengths and limitations, and highlight potential directions for future research. Generative Artificial Intelligence (Generative AI) has revolutionized the fusion of textual information and visual content, giving rise to sop

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Budaev, E. V. "Linguocognitive Approach to Text Analysis." Book. Reading. Media 1, no. 1 (2024): 21–28. http://dx.doi.org/10.20913/brm-1-1-3.

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Cognitive sciences were officially included in the nomenclature of scientific specialties in Russia in 2021. However, the issue of the subject and method of cognitive science in general and its projection on text analysis in particular remains little known to the general scientific community. Linguistic text analysis has gone through several stages of evolution. Each of them accumulates the critical aspects highlighting the necessity to search for new approaches. There are three such approaches in linguistics: structural, communicative, cognitive. The structural paradigm made it possible to so

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