Zobrazeno 1 - 10
of 1 385
pro vyhledávání: '"Getoor"'
Autor:
Dickens, Charles, Pryor, Connor, Gao, Changyu, Albalak, Alon, Augustine, Eriq, Wang, William, Wright, Stephen, Getoor, Lise
The field of Neural-Symbolic (NeSy) systems is growing rapidly. Proposed approaches show great promise in achieving symbiotic unions of neural and symbolic methods. However, each NeSy system differs in fundamental ways. There is a pressing need for a
Externí odkaz:
http://arxiv.org/abs/2407.09693
Autor:
Pryor, Connor, Yuan, Quan, Liu, Jeremiah, Kazemi, Mehran, Ramachandran, Deepak, Bedrax-Weiss, Tania, Getoor, Lise
Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i.e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog. It is a critical component for modern dialog system design and discou
Externí odkaz:
http://arxiv.org/abs/2403.17853
We leverage convex and bilevel optimization techniques to develop a general gradient-based parameter learning framework for neural-symbolic (NeSy) systems. We demonstrate our framework with NeuPSL, a state-of-the-art NeSy architecture. To achieve thi
Externí odkaz:
http://arxiv.org/abs/2401.09651
Autor:
Zhou, Kaiwen, Zheng, Kaizhi, Pryor, Connor, Shen, Yilin, Jin, Hongxia, Getoor, Lise, Wang, Xin Eric
The ability to accurately locate and navigate to a specific object is a crucial capability for embodied agents that operate in the real world and interact with objects to complete tasks. Such object navigation tasks usually require large-scale traini
Externí odkaz:
http://arxiv.org/abs/2301.13166
Autor:
Tuan, Yi-Lin, Albalak, Alon, Xu, Wenda, Saxon, Michael, Pryor, Connor, Getoor, Lise, Wang, William Yang
Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans. In this research, we examine user utterances as causes and generated responses as effects, recognizing that changes in a cause sh
Externí odkaz:
http://arxiv.org/abs/2212.10515
Autor:
Augustine, Eriq, Jandaghi, Pegah, Albalak, Alon, Pryor, Connor, Dickens, Charles, Wang, William, Getoor, Lise
Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community. A recent pillar of research revolves around emotion recog
Externí odkaz:
http://arxiv.org/abs/2207.07238
In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks. To model the boundary between neural
Externí odkaz:
http://arxiv.org/abs/2205.14268
Autor:
Albalak, Alon, Tuan, Yi-Lin, Jandaghi, Pegah, Pryor, Connor, Yoffe, Luke, Ramachandran, Deepak, Getoor, Lise, Pujara, Jay, Wang, William Yang
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue understanding encompasses many diverse tasks, yet task transfer has not been
Externí odkaz:
http://arxiv.org/abs/2205.06262
Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods. This work addresses that gap by focusing on extracting e
Externí odkaz:
http://arxiv.org/abs/2109.05126
In comparison to the interpretation of classification models, the explanation of sequence generation models is also an important problem, however it has seen little attention. In this work, we study model-agnostic explanations of a representative tex
Externí odkaz:
http://arxiv.org/abs/2106.06528