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of 55
pro vyhledávání: '"Weischedel, Ralph"'
Autor:
Wu, Te-Lin, Dou, Zi-Yi, Hu, Qingyuan, Hou, Yu, Chandra, Nischal Reddy, Freedman, Marjorie, Weischedel, Ralph M., Peng, Nanyun
Multimodal counterfactual reasoning is a vital yet challenging ability for AI systems. It involves predicting the outcomes of hypothetical circumstances based on vision and language inputs, which enables AI models to learn from failures and explore h
Externí odkaz:
http://arxiv.org/abs/2311.01620
Autor:
Ciosici, Manuel R., Hedges, Alex, Kankanampati, Yash, Martin, Justin, Freedman, Marjorie, Weischedel, Ralph
We explore using a moderately sized large language model (GPT-J 6B parameters) to create a plan for a simulated robot to achieve 30 classes of goals in ScienceWorld, a text game simulator for elementary science experiments. Previously published empir
Externí odkaz:
http://arxiv.org/abs/2311.01468
Autor:
Wu, Te-Lin, Spangher, Alex, Alipoormolabashi, Pegah, Freedman, Marjorie, Weischedel, Ralph, Peng, Nanyun
The ability to sequence unordered events is an essential skill to comprehend and reason about real world task procedures, which often requires thorough understanding of temporal common sense and multimodal information, as these procedures are often c
Externí odkaz:
http://arxiv.org/abs/2110.08486
Autor:
Ciosici, Manuel R., Cecil, Joe, Hedges, Alex, Lee, Dong-Ho, Freedman, Marjorie, Weischedel, Ralph
Our goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e.g., an introductory college textbook or a manual. PTLMs have s
Externí odkaz:
http://arxiv.org/abs/2110.01552
With the recent advances of open-domain story generation, the lack of reliable automatic evaluation metrics becomes an increasingly imperative issue that hinders the fast development of story generation. According to conducted researches in this rega
Externí odkaz:
http://arxiv.org/abs/2104.05801
Autor:
Ciosici, Manuel R., Cummings, Joseph, DeHaven, Mitchell, Hedges, Alex, Kankanampati, Yash, Lee, Dong-Ho, Weischedel, Ralph, Freedman, Marjorie
We describe Machine-Aided Script Curator (MASC), a system for human-machine collaborative script authoring. Scripts produced with MASC include (1) English descriptions of sub-events that comprise a larger, complex event; (2) event types for each of t
Externí odkaz:
http://arxiv.org/abs/2101.05400
We consider problems of making sequences of decisions to accomplish tasks, interacting via the medium of language. These problems are often tackled with reinforcement learning approaches. We find that these models do not generalize well when applied
Externí odkaz:
http://arxiv.org/abs/2010.02229
Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global cohesion. We posit that many of the problems of story generation can be
Externí odkaz:
http://arxiv.org/abs/2009.09870
User engagement is a critical metric for evaluating the quality of open-domain dialogue systems. Prior work has focused on conversation-level engagement by using heuristically constructed features such as the number of turns and the total time of the
Externí odkaz:
http://arxiv.org/abs/1911.01456
We propose a novel deep structured learning framework for event temporal relation extraction. The model consists of 1) a recurrent neural network (RNN) to learn scoring functions for pair-wise relations, and 2) a structured support vector machine (SS
Externí odkaz:
http://arxiv.org/abs/1909.10094