Asking It All: Generating Contextualized Questions for any Semantic Role
Asking It All: Generating Contextualized Questions for any Semantic Role
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询问一切：针对任何语义角色生成情境化问题
DOI：
10.18653/v1/2021.emnlp-main.108
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Ido Dagan
中科院分区：
文献类型：
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作者：
Valentina Pyatkin;Paul Roit;Julian Michael;Reut Tsarfaty;Yoav Goldberg;Ido Dagan
提出有关情况的问题是理解它的固有步骤。为此，我们介绍了角色问题生成的任务，鉴于谓词提及和段落，需要提出一组问题，询问谓词的所有可能的语义角色。我们为此任务开发了一个两阶段的模型，该模型首先为每个角色生成独立于上下文的问题原型，然后将其修改为在上下文上适合段落。与大多数现有的提问生成方法不同，我们的方法不需要根据文本中的现有答案进行条件。取而代之的是，无论答案是否明确出现在文本中，我们都可以从中推断出来，还是应在其他地方寻找答案。我们的评估表明，我们为谓词和角色的大型覆盖本体论产生了多样化且形成良好的问题。
Asking questions about a situation is an inherent step towards understanding it. To this end, we introduce the task of role question generation, which, given a predicate mention and a passage, requires producing a set of questions asking about all possible semantic roles of the predicate. We develop a two-stage model for this task, which first produces a context-independent question prototype for each role and then revises it to be contextually appropriate for the passage. Unlike most existing approaches to question generation, our approach does not require conditioning on existing answers in the text. Instead, we condition on the type of information to inquire about, regardless of whether the answer appears explicitly in the text, could be inferred from it, or should be sought elsewhere. Our evaluation demonstrates that we generate diverse and well-formed questions for a large, broad-coverage ontology of predicates and roles.