A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit <a rel="external noopener" href="http://pdfs.semanticscholar.org/0510/e593219bdadebdb3ff203d1ff8117f28fb3a.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<i title="Association for Computational Linguistics">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5n6volmnonf5tn6xputi5f2t3e" style="color: black;">Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</a>
To enable human-robot communication and collaboration, previous works represent grounded verb semantics as the potential change of state to the physical world caused by these verbs. Grounded verb semantics are acquired mainly based on the parallel data of the use of a verb phrase and its corresponding sequences of primitive actions demonstrated by humans. The rich interaction between teachers and students that is considered important in learning new skills has not yet been explored. To address<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/p17-1150">doi:10.18653/v1/p17-1150</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/acl/SheC17.html">dblp:conf/acl/SheC17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2nk6x3f3bbbftgd6x6ieqt7jle">fatcat:2nk6x3f3bbbftgd6x6ieqt7jle</a> </span>
more »... his limitation, this paper presents a new interactive learning approach that allows robots to proactively engage in interaction with human partners by asking good questions to learn models for grounded verb semantics. The proposed approach uses reinforcement learning to allow the robot to acquire an optimal policy for its question-asking behaviors by maximizing the long-term reward. Our empirical results have shown that the interactive learning approach leads to more reliable models for grounded verb semantics, especially in the noisy environment which is full of uncertainties. Compared to previous work, the models acquired from interactive learning result in a 48% to 145% performance gain when applied in new situations.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190217115208/http://pdfs.semanticscholar.org/0510/e593219bdadebdb3ff203d1ff8117f28fb3a.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/05/10/0510e593219bdadebdb3ff203d1ff8117f28fb3a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/p17-1150"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>