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Wh-Questions are understood before polar-questions: Evidence from English, German, and Chinese

Published online by Cambridge University Press:  28 May 2020

Sara MORADLOU
Affiliation:
Université de Paris, CNRS, LLF, France
Xiaobei ZHENG
Affiliation:
School of Foreign Languages, Shenzhen University, China
Ye TIAN
Affiliation:
Université de Paris, CNRS, LLF, France MediaTek Research, Cambridge, UK
Jonathan GINZBURG*
Affiliation:
Université de Paris, CNRS, LLF, France
*
*Address for correspondence: Jonathan Ginzburg, Email: yonatan.ginzburg@u-paris.fr
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Abstract

In this paper we consider the order of emergence of comprehension of wh-questions and polar-questions. We argue that considerations of complexity and input favour the earlier emergence of polar questions; on the other hand, if one assumes that question understanding emerges as a consequence of interactive learning this favours (certain) wh-questions, as well as a small subclass of polar questions. We offer corpus evidence from the Providence corpus that (a certain class of) wh-questions are in fact understood earlier than the polar-questions. We test this observation using elicitation studies on German and Chinese speaking children. Our results confirm the finding from the corpus study and are in line with an interactive learning perspective for the emergence of understanding of questions.

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Type
Article
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press
Figure 0

Table 1. Answering rates for Lily, Naima, and Violet of Providence corpus

Figure 1

Table 2. Self answering rate for polars and wh-questions

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Figure 1. (l) Is this a hen? What does the hen say?(r) Is this a duck? What does the duck say?

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Table 3. Coding dimensions of responses provided by children

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Table 4. Mean scores attained by each child for wh-questions and the differential from means for polars; German (l), Chinese (r), ordered by size of wh−polar differential

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Figure 2. Average scores for wh vs polar-yes and polar-no

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Figure 3. Average scores for experimental conditions

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Table 5. Best fitting models

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Figure 4. Average scores for question types