Informational content vs. discourse orientation: experimental and computational perspectives

G. Winterstein, Ghyslain Cantin-Savoie, Samuel Laperle, Josiane Van Dorpe, Nora Villeneuve
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Abstract

The aim of this study is to investigate how human speakers and computational language models process (i) the informational content and (ii) the discourse orientation of natural language sentences. These two dimensions of meaning have received little attention outside theoretical literature, especially in the computational linguistics domain. To help fill this void, we present the results of four experiments that exploit the specific semantics of two French adverbs, namely presque (≃ ’almost’) and à peine (≃ ’barely’), which put these two dimensions of meaning at odds. Each experiment focuses on one kind of population (humans or language models), and one kind of meaning (informational content or discourse orientation). Our results show that humans are indeed sensitive to informational content and discourse direction, as assumed in the theoretical literature. Language models exhibit a less transparent behavior. Their performances in dealing with the semantics of presque appear in line with predictions based on the way these models are trained, but this does not extend to à peine.
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信息内容与话语取向:实验与计算视角
本研究的目的是探讨人类说话者和计算语言模型如何处理自然语言句子的信息内容和话语取向。这两个意义维度在理论文献之外很少受到关注,特别是在计算语言学领域。为了帮助填补这一空白,我们提出了四个实验的结果,这些实验利用了两个法语副词的特定语义,即presque(“几乎”)和peine(“几乎”),这两个维度的意义是不一致的。每个实验都关注一种群体(人类或语言模型)和一种意义(信息内容或话语取向)。我们的研究结果表明,正如理论文献所假设的那样,人类确实对信息内容和话语方向敏感。语言模型表现出一种不太透明的行为。他们在处理先验语义方面的表现似乎与基于这些模型训练方式的预测一致,但这并没有扩展到在线。
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