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<span lang="en-US" style="margin:0px;font-family:Arial, sans-serif">Hi Adapters,<span style="margin:0px"> </span></span></p>
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This time, I will introduce a paper accepted by EMNLP2020: Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph.
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Despite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. The writers argue that exploiting both the structural
and semantic information of the knowledge graph facilitates commonsenseaware text generation. In this paper, the writers propose Generation with Multi-Hop Reasoning Flow (GRF) that enables pre-trained models with dynamic multi-hop reasoning on multirelational
paths extracted from the external commonsense knowledge graph. They empirically show that their model outperforms existing baselines on three text generation tasks that require reasoning over commonsense knowledge. They also demonstrate the effectiveness of
the dynamic multi-hop reasoning module with reasoning paths inferred by the model that provide rationale to the generation.<br>
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<pre style="font-size:12pt;font-family:SimSun;margin:0px"><span lang="en-US" style="margin:0px;font-size:10.5pt">Time: Wed 4:00pm </span></pre>
<pre style="font-size:12pt;font-family:SimSun;margin:0px"><span lang="en-US" style="margin:0px;font-size:10.5pt">Venue: SEIEE 3-414
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<font face="DengXian"><span style="margin:0px;font-size:14px">Zitong</span></font></div>
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