Why Can't Discourse Parsing Generalize? A Thorough Investigation of the
Impact of Data Diversity
- URL: http://arxiv.org/abs/2302.06488v1
- Date: Mon, 13 Feb 2023 16:11:58 GMT
- Title: Why Can't Discourse Parsing Generalize? A Thorough Investigation of the
Impact of Data Diversity
- Authors: Yang Janet Liu and Amir Zeldes
- Abstract summary: We show that state-of-the-art architectures trained on the standard English newswire benchmark do not generalize well.
We quantify the impact of genre diversity in training data for achieving generalization to text types unseen.
To our knowledge, this study is the first to fully evaluate cross-corpus RST parsing generalizability on complete trees.
- Score: 10.609715843964263
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in discourse parsing performance create the impression that,
as in other NLP tasks, performance for high-resource languages such as English
is finally becoming reliable. In this paper we demonstrate that this is not the
case, and thoroughly investigate the impact of data diversity on RST parsing
stability. We show that state-of-the-art architectures trained on the standard
English newswire benchmark do not generalize well, even within the news domain.
Using the two largest RST corpora of English with text from multiple genres, we
quantify the impact of genre diversity in training data for achieving
generalization to text types unseen during training. Our results show that a
heterogeneous training regime is critical for stable and generalizable models,
across parser architectures. We also provide error analyses of model outputs
and out-of-domain performance. To our knowledge, this study is the first to
fully evaluate cross-corpus RST parsing generalizability on complete trees,
examine between-genre degradation within an RST corpus, and investigate the
impact of genre diversity in training data composition.
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