Littérature scientifique sur le sujet « Sentence Embedding Spaces »
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Articles de revues sur le sujet "Sentence Embedding Spaces"
Nguyen, Huy Manh, Tomo Miyazaki, Yoshihiro Sugaya et Shinichiro Omachi. « Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence ». Applied Sciences 11, no 7 (3 avril 2021) : 3214. http://dx.doi.org/10.3390/app11073214.
Texte intégralLiu, Yi, Chengyu Yin, Jingwei Li, Fang Wang et Senzhang Wang. « Predicting Dynamic User–Item Interaction with Meta-Path Guided Recursive RNN ». Algorithms 15, no 3 (28 février 2022) : 80. http://dx.doi.org/10.3390/a15030080.
Texte intégralQian, Chen, Fuli Feng, Lijie Wen et Tat-Seng Chua. « Conceptualized and Contextualized Gaussian Embedding ». Proceedings of the AAAI Conference on Artificial Intelligence 35, no 15 (18 mai 2021) : 13683–91. http://dx.doi.org/10.1609/aaai.v35i15.17613.
Texte intégralCantini, Riccardo, Fabrizio Marozzo, Giovanni Bruno et Paolo Trunfio. « Learning Sentence-to-Hashtags Semantic Mapping for Hashtag Recommendation on Microblogs ». ACM Transactions on Knowledge Discovery from Data 16, no 2 (30 avril 2022) : 1–26. http://dx.doi.org/10.1145/3466876.
Texte intégralZhang, Yachao, Runze Hu, Ronghui Li, Yanyun Qu, Yuan Xie et Xiu Li. « Cross-Modal Match for Language Conditioned 3D Object Grounding ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 7 (24 mars 2024) : 7359–67. http://dx.doi.org/10.1609/aaai.v38i7.28566.
Texte intégralDancygier, Barbara. « Mental space embeddings, counterfactuality, and the use of unless ». English Language and Linguistics 6, no 2 (9 octobre 2002) : 347–77. http://dx.doi.org/10.1017/s1360674302000278.
Texte intégralAmigo, Enrique, Alejandro Ariza-Casabona, Victor Fresno et M. Antonia Marti. « Information Theory–based Compositional Distributional Semantics ». Computational Linguistics 48, no 4 (2022) : 907–48. http://dx.doi.org/10.1162/_.
Texte intégralFaraz, Anum, Fardin Ahsan, Jinane Mounsef, Ioannis Karamitsos et Andreas Kanavos. « Enhancing Child Safety in Online Gaming : The Development and Application of Protectbot, an AI-Powered Chatbot Framework ». Information 15, no 4 (19 avril 2024) : 233. http://dx.doi.org/10.3390/info15040233.
Texte intégralCroce, Danilo, Giuseppe Castellucci et Roberto Basili. « Adversarial training for few-shot text classification ». Intelligenza Artificiale 14, no 2 (11 janvier 2021) : 201–14. http://dx.doi.org/10.3233/ia-200051.
Texte intégralHao, Sun, Xiaolin Qin et Xiaojing Liu. « Learning hierarchical embedding space for image-text matching ». Intelligent Data Analysis, 14 septembre 2023, 1–19. http://dx.doi.org/10.3233/ida-230214.
Texte intégralThèses sur le sujet "Sentence Embedding Spaces"
Duquenne, Paul-Ambroise. « Sentence Embeddings for Massively Multilingual Speech and Text Processing ». Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS039.
Texte intégralRepresentation learning of sentences has been widely studied in NLP. While many works have explored different pre-training objectives to create contextual representations from sentences, several others have focused on learning sentence embeddings for multiple languages with the aim of closely encoding paraphrases and translations in the sentence embedding space.In this thesis, we first study how to extend text sentence embedding spaces to the speech modality in order to build a multilingual speech/text sentence embedding space. Next, we explore how to use this multilingual and multimodal sentence embedding space for large-scale speech mining. This allows us to automatically create alignments between written and spoken sentences in different languages. For high similarity thresholds in the latent space, aligned sentences can be considered as translations. If the alignments involve written sentences on one side and spoken sentences on the other, then these are potential speech-to-text translations. If the alignments involve on both sides spoken sentences, then these are potential speech-to-speech translations. To validate the quality of the mined data, we train speech-to-text translation models and speech-to-speech translation models. We show that adding the automatically mined data significantly improves the quality of the learned translation models, demonstrating the quality of the alignments and the usefulness of the mined data.Then, we study how to decode these sentence embeddings into text or speech in different languages. We explore several methods for training decoders and analyze their robustness to modalities/languages not seen during training, to evaluate cross-lingual and cross-modal transfers. We demonstrate that we could perform zero-shot cross-modal translation in this framework, achieving translation results close to systems learned in a supervised manner with a cross-attention mechanism. The compatibility between speech/text representations from different languages enables these very good performances, despite an intermediate fixed-size representation.Finally, we develop a new state-of-the-art massively multilingual speech/text sentence embedding space, named SONAR, based on conclusions drawn from the first two projects. We study different objective functions to learn such a space and we analyze their impact on the organization of the space as well as on the capabilities to decode these representations. We show that such sentence embedding space outperform previous state-of-the-art methods for both cross-lingual and cross-modal similarity search as well as decoding capabilities. This new space covers 200 written languages and 37 spoken languages. It also offers text translation results close to the NLLB system on which it is based, and speech translation results competitive with the Whisper supervised system. We also present SONAR EXPRESSIVE, which introduces an additional representation encoding non-semantic speech properties, such as vocal style or expressivity of speech
Chapitres de livres sur le sujet "Sentence Embedding Spaces"
Alnajjar, Khalid. « When Word Embeddings Become Endangered ». Dans Multilingual Facilitation, 275–88. University of Helsinki, 2021. http://dx.doi.org/10.31885/9789515150257.24.
Texte intégralXiao, Qingfa, Shuangyin Li et Lei Chen. « Identical and Fraternal Twins : Fine-Grained Semantic Contrastive Learning of Sentence Representations ». Dans Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230584.
Texte intégralActes de conférences sur le sujet "Sentence Embedding Spaces"
Zhang, Chengkun, et Junbin Gao. « Hype-HAN : Hyperbolic Hierarchical Attention Network for Semantic Embedding ». Dans Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California : International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/552.
Texte intégralWei, Liangchen, et Zhi-Hong Deng. « A Variational Autoencoding Approach for Inducing Cross-lingual Word Embeddings ». Dans Twenty-Sixth International Joint Conference on Artificial Intelligence. California : International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/582.
Texte intégralXu, Linli, Wenjun Ouyang, Xiaoying Ren, Yang Wang et Liang Jiang. « Enhancing Semantic Representations of Bilingual Word Embeddings with Syntactic Dependencies ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/628.
Texte intégralBaumel, Tal, Raphael Cohen et Michael Elhadad. « Sentence Embedding Evaluation Using Pyramid Annotation ». Dans Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP. Stroudsburg, PA, USA : Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/w16-2526.
Texte intégralYi, Xiaoyuan, Zhenghao Liu, Wenhao Li et Maosong Sun. « Text Style Transfer via Learning Style Instance Supported Latent Space ». Dans Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California : International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/526.
Texte intégralAn, Yuan, Alexander Kalinowski et Jane Greenberg. « Clustering and Network Analysis for the Embedding Spaces of Sentences and Sub-Sentences ». Dans 2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA). IEEE, 2021. http://dx.doi.org/10.1109/idsta53674.2021.9660801.
Texte intégralSato, Motoki, Jun Suzuki, Hiroyuki Shindo et Yuji Matsumoto. « Interpretable Adversarial Perturbation in Input Embedding Space for Text ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/601.
Texte intégralHwang, Eugene. « Saving Endangered Languages with a Novel Three-Way Cycle Cross-Lingual Zero-Shot Sentence Alignment ». Dans 10th International Conference on Artificial Intelligence & Applications. Academy & Industry Research Collaboration Center, 2023. http://dx.doi.org/10.5121/csit.2023.131926.
Texte intégralLi, Wenye, Jiawei Zhang, Jianjun Zhou et Laizhong Cui. « Learning Word Vectors with Linear Constraints : A Matrix Factorization Approach ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/582.
Texte intégralDimovski, Mladen, Claudiu Musat, Vladimir Ilievski, Andreea Hossman et Michael Baeriswyl. « Submodularity-Inspired Data Selection for Goal-Oriented Chatbot Training Based on Sentence Embeddings ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/559.
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