Replicate – Run open-source machine learning models with a cloud API
AI Tool Profile
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction
Cross-lingual open information extraction aims to extract structured information from raw text across multiple languages.
GitHub Link
The GitHub link is https://github.com/CSJianYang/Multilingual-Multimodal-NLP/tree/main/MT4CrossOIEIntroduce
Cross-lingual open information extraction aims to extract structured information from raw text across multiple languages.Content
Cross-lingual open information extraction aims to extract structured information from raw text across multiple languages. Previous work uses a shared cross-lingual pre-trained model to handle the different languages but underuses the potential of the language-specific representation. In this paper, we propose an effective multi-stage tuning framework called MT4CrossOIE, designed for enhancing cross-lingual open information extraction by injecting language-specific knowledge into the shared model. Specifically, the cross-lingual pre-trained model is first tuned in a shared semantic space (e.g., embedding matrix) in the fixed encoder and then other components are optimized in the second stage. After enough training, we freeze the pre-trained model and tune the multiple extra low-rank language-specific modules using mixture-of- LoRAs for model-based cross-lingual transfer. In addition, we leverage two-stage prompting to encourage the large language model (LLM) to annotate the multilingual raw data for data-based cross-lingual transfer. The model is trained with multilingual objectives on our proposed dataset OpenIE4++ by combing the model-based and data-based transfer techniques. Experimental results on various benchmarks emphasize the importance of aggregating multiple plug-in-and-play languagespecific modules and demonstrate the effectiveness of MT4CrossOIE in cross-lingual OIE.Alternatives & Similar Tools
Google Gemini, a multimodal AI by DeepMind, processes text, audio, images, and more. Gemini outperforms in AI benchmarks, is optimized for varied devices, and has been tested for safety and bias, adhering to responsible AI practices.
LongLLaMA is a large language model designed to handle very long text contexts, up to 256,000 tokens. It's based on OpenLLaMA and uses a technique called Focused Transformer (FoT) for training. The repository provides a smaller 3B version of LongLLaMA for free use. It can also be used as a replacement for LLaMA models with shorter contexts.
LAMA utilizes a reinforcement learning framework combined with a motion matching algorithm. Reinforcement learning helps the model make appropriate decisions in various scenarios, while motion matching algorithms ensure that synthesized actions match real human actions. In addition, LAMA also utilizes the motion editing framework of manifold learning to cover various possible changes in interactions and operations.
Video ReTalking-focuses on audio-based lip synchronization for talking head video editing Open Source
Video ReTalking, advanced real-world talking head video according to input audio, producing a high-quality
UniSim-Chat Control Video and Virtual simulation Open Source
Then transplant it to the real world to solve complex problems
Compare MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction
Quick compare routes for nearby alternatives.
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction vs Replicate-AI model GFPGAN can help restore old photos
Compare MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction with Replicate-AI model GFPGAN can help restore old photos and jump into the preserved compare route.
Open compare route →MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction vs Free Google Gemini: the best largest and most capable AI model
Compare MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction with Free Google Gemini: the best largest and most capable AI model and jump into the preserved compare route.
Open compare route →MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction vs LongLLaMA-handle very long text contexts, up to 256,000 tokens
Compare MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction with LongLLaMA-handle very long text contexts, up to 256,000 tokens and jump into the preserved compare route.
Open compare route →