most citedLexically Constrained Decoding for Sequence Generation Using Grid Beam Search

34 citations

5 papers

cs.LG2022★ 11 cited

Interpretable Hidden Markov Model-Based Deep Reinforcement Learning Hierarchical Framework for Predictive Maintenance of Turbofan Engines

Ammar N. Abbas, Georgios Chasparis, John D. Kelleher

An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes combining the advantages…

physics.soc-ph2022★ 1 cited

Simulating Delay in Seeking Treatment for Stroke due to COVID-19 Concerns with a Hybrid Agent-Based and Equation-Based Model

Elizabeth Hunter, Bryony L. McGarry, John D. Kelleher

COVID-19 has caused tremendous strain on healthcare systems worldwide. At the same time, concern within the population over this strain and the chances of becoming infected has pot…

cs.CL2017★ 28 cited

Evaluation of Automatic Video Captioning Using Direct Assessment

Yvette Graham, George Awad, Alan Smeaton

We present Direct Assessment, a method for manually assessing the quality of automatically-generated captions for video. Evaluating the accuracy of video captions is particularly d…

cs.CL2017★ 34 cited

Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search

Chris Hokamp, Qun Liu

We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model that…

cs.IR2017★ 3 cited

Real-World Recommender Systems for Academia: The Pain and Gain in Building, Operating, and Researching them [Long Version]

Joeran Beel, Siddharth Dinesh

Research on recommender systems is a challenging task, as is building and operating such systems. Major challenges include non-reproducible research results, dealing with noisy dat…