Gotowa bibliografia na temat „Sentence Embedding Spaces”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Sentence Embedding Spaces”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Sentence Embedding Spaces"
Nguyen, Huy Manh, Tomo Miyazaki, Yoshihiro Sugaya i Shinichiro Omachi. "Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence". Applied Sciences 11, nr 7 (3.04.2021): 3214. http://dx.doi.org/10.3390/app11073214.
Pełny tekst źródłaLiu, Yi, Chengyu Yin, Jingwei Li, Fang Wang i Senzhang Wang. "Predicting Dynamic User–Item Interaction with Meta-Path Guided Recursive RNN". Algorithms 15, nr 3 (28.02.2022): 80. http://dx.doi.org/10.3390/a15030080.
Pełny tekst źródłaQian, Chen, Fuli Feng, Lijie Wen i Tat-Seng Chua. "Conceptualized and Contextualized Gaussian Embedding". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 15 (18.05.2021): 13683–91. http://dx.doi.org/10.1609/aaai.v35i15.17613.
Pełny tekst źródłaCantini, Riccardo, Fabrizio Marozzo, Giovanni Bruno i Paolo Trunfio. "Learning Sentence-to-Hashtags Semantic Mapping for Hashtag Recommendation on Microblogs". ACM Transactions on Knowledge Discovery from Data 16, nr 2 (30.04.2022): 1–26. http://dx.doi.org/10.1145/3466876.
Pełny tekst źródłaZhang, Yachao, Runze Hu, Ronghui Li, Yanyun Qu, Yuan Xie i Xiu Li. "Cross-Modal Match for Language Conditioned 3D Object Grounding". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 7 (24.03.2024): 7359–67. http://dx.doi.org/10.1609/aaai.v38i7.28566.
Pełny tekst źródłaDancygier, Barbara. "Mental space embeddings, counterfactuality, and the use of unless". English Language and Linguistics 6, nr 2 (9.10.2002): 347–77. http://dx.doi.org/10.1017/s1360674302000278.
Pełny tekst źródłaAmigo, Enrique, Alejandro Ariza-Casabona, Victor Fresno i M. Antonia Marti. "Information Theory–based Compositional Distributional Semantics". Computational Linguistics 48, nr 4 (2022): 907–48. http://dx.doi.org/10.1162/_.
Pełny tekst źródłaFaraz, Anum, Fardin Ahsan, Jinane Mounsef, Ioannis Karamitsos i Andreas Kanavos. "Enhancing Child Safety in Online Gaming: The Development and Application of Protectbot, an AI-Powered Chatbot Framework". Information 15, nr 4 (19.04.2024): 233. http://dx.doi.org/10.3390/info15040233.
Pełny tekst źródłaCroce, Danilo, Giuseppe Castellucci i Roberto Basili. "Adversarial training for few-shot text classification". Intelligenza Artificiale 14, nr 2 (11.01.2021): 201–14. http://dx.doi.org/10.3233/ia-200051.
Pełny tekst źródłaHao, Sun, Xiaolin Qin i Xiaojing Liu. "Learning hierarchical embedding space for image-text matching". Intelligent Data Analysis, 14.09.2023, 1–19. http://dx.doi.org/10.3233/ida-230214.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaRepresentation 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
Części książek na temat "Sentence Embedding Spaces"
Alnajjar, Khalid. "When Word Embeddings Become Endangered". W Multilingual Facilitation, 275–88. University of Helsinki, 2021. http://dx.doi.org/10.31885/9789515150257.24.
Pełny tekst źródłaXiao, Qingfa, Shuangyin Li i Lei Chen. "Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations". W Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230584.
Pełny tekst źródłaStreszczenia konferencji na temat "Sentence Embedding Spaces"
Zhang, Chengkun, i Junbin Gao. "Hype-HAN: Hyperbolic Hierarchical Attention Network for Semantic Embedding". W 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.
Pełny tekst źródłaWei, Liangchen, i Zhi-Hong Deng. "A Variational Autoencoding Approach for Inducing Cross-lingual Word Embeddings". W 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.
Pełny tekst źródłaXu, Linli, Wenjun Ouyang, Xiaoying Ren, Yang Wang i Liang Jiang. "Enhancing Semantic Representations of Bilingual Word Embeddings with Syntactic Dependencies". W 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.
Pełny tekst źródłaBaumel, Tal, Raphael Cohen i Michael Elhadad. "Sentence Embedding Evaluation Using Pyramid Annotation". W 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.
Pełny tekst źródłaYi, Xiaoyuan, Zhenghao Liu, Wenhao Li i Maosong Sun. "Text Style Transfer via Learning Style Instance Supported Latent Space". W 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.
Pełny tekst źródłaAn, Yuan, Alexander Kalinowski i Jane Greenberg. "Clustering and Network Analysis for the Embedding Spaces of Sentences and Sub-Sentences". W 2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA). IEEE, 2021. http://dx.doi.org/10.1109/idsta53674.2021.9660801.
Pełny tekst źródłaSato, Motoki, Jun Suzuki, Hiroyuki Shindo i Yuji Matsumoto. "Interpretable Adversarial Perturbation in Input Embedding Space for Text". W 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.
Pełny tekst źródłaHwang, Eugene. "Saving Endangered Languages with a Novel Three-Way Cycle Cross-Lingual Zero-Shot Sentence Alignment". W 10th International Conference on Artificial Intelligence & Applications. Academy & Industry Research Collaboration Center, 2023. http://dx.doi.org/10.5121/csit.2023.131926.
Pełny tekst źródłaLi, Wenye, Jiawei Zhang, Jianjun Zhou i Laizhong Cui. "Learning Word Vectors with Linear Constraints: A Matrix Factorization Approach". W 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.
Pełny tekst źródłaDimovski, Mladen, Claudiu Musat, Vladimir Ilievski, Andreea Hossman i Michael Baeriswyl. "Submodularity-Inspired Data Selection for Goal-Oriented Chatbot Training Based on Sentence Embeddings". W 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.
Pełny tekst źródła