Authors:
Jean-Philippe Bernardy
and
Stergios Chatzikyriakidis
Affiliation:
CLASP, Department of Philosophy, Linguistics and Theory of Science, University of Gothenburg and Sweden
Keyword(s):
Natural Language Inference, Textual Entailment, Reasoning in Dialogue, Datasets, SNLI, RTE.
Abstract:
In this paper, we look at Natural Language Inference, arguing that the notion of inference the current NLP systems are learning is much narrower compared to the range of inference patterns found in human reasoning. We take a look at the history and the nature of creating datasets for NLI. We discuss the datasets that are mainly used today for the relevant tasks and show why those are not enough to generalize to other reasoning tasks, e.g. logical and legal reasoning, or reasoning in dialogue settings. We then proceed to propose ways in which this can be remedied, effectively producing more realistic datasets for NLI. Lastly, we argue that the NLP community could have been too hasty to altogether dismiss symbolic approaches in the study of NLI, given that these might still be relevant for more fine-grained cases of reasoning. As such, we argue for a more pluralistic take on tackling NLI, favoring hybrid rather than non-hybrid approaches.