반응형 논문572 [2023-04-17] 오늘의 자연어처리 Emergence of Symbols in Neural Networks for Semantic Understanding and Communication Being able to create meaningful symbols and proficiently use them for higher cognitive functions such as communication, reasoning, planning, etc., is essential and unique for human intelligence. Current deep neural networks are still far behind human's ability to create symbols for such higher cognitive function.. 2023. 4. 17. [2023-04-16] 오늘의 자연어처리 Sign Language Translation from Instructional Videos The advances in automatic sign language translation (SLT) to spoken languages have been mostly benchmarked with datasets of limited size and restricted domains. Our work advances the state of the art by providing the first baseline results on How2Sign, a large and broad dataset. We train a Transformer over I3D video features, using the reduced .. 2023. 4. 16. [2023-04-15] 오늘의 자연어처리 PGTask: Introducing the Task of Profile Generation from Dialogues Recent approaches have attempted to personalize dialogue systems by leveraging profile information into models. However, this knowledge is scarce and difficult to obtain, which makes the extraction/generation of profile information from dialogues a fundamental asset. To surpass this limitation, we introduce the Profile Generation .. 2023. 4. 15. [2023-04-14] 오늘의 자연어처리 Understanding Causality with Large Language Models: Feasibility and Opportunities We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question. We believe that current LLMs can answer causal questions with existing causal knowledge as combined domain experts. However, they are not yet able to p.. 2023. 4. 14. 이전 1 ··· 67 68 69 70 71 72 73 ··· 143 다음 반응형