337 research outputs found
Syntactic Data Augmentation Increases Robustness to Inference Heuristics
Pretrained neural models such as BERT, when fine-tuned to perform natural
language inference (NLI), often show high accuracy on standard datasets, but
display a surprising lack of sensitivity to word order on controlled challenge
sets. We hypothesize that this issue is not primarily caused by the pretrained
model's limitations, but rather by the paucity of crowdsourced NLI examples
that might convey the importance of syntactic structure at the fine-tuning
stage. We explore several methods to augment standard training sets with
syntactically informative examples, generated by applying syntactic
transformations to sentences from the MNLI corpus. The best-performing
augmentation method, subject/object inversion, improved BERT's accuracy on
controlled examples that diagnose sensitivity to word order from 0.28 to 0.73,
without affecting performance on the MNLI test set. This improvement
generalized beyond the particular construction used for data augmentation,
suggesting that augmentation causes BERT to recruit abstract syntactic
representations.Comment: ACL 202
State-of-the-art generalisation research in NLP: a taxonomy and review
The ability to generalise well is one of the primary desiderata of natural
language processing (NLP). Yet, what `good generalisation' entails and how it
should be evaluated is not well understood, nor are there any common standards
to evaluate it. In this paper, we aim to lay the ground-work to improve both of
these issues. We present a taxonomy for characterising and understanding
generalisation research in NLP, we use that taxonomy to present a comprehensive
map of published generalisation studies, and we make recommendations for which
areas might deserve attention in the future. Our taxonomy is based on an
extensive literature review of generalisation research, and contains five axes
along which studies can differ: their main motivation, the type of
generalisation they aim to solve, the type of data shift they consider, the
source by which this data shift is obtained, and the locus of the shift within
the modelling pipeline. We use our taxonomy to classify over 400 previous
papers that test generalisation, for a total of more than 600 individual
experiments. Considering the results of this review, we present an in-depth
analysis of the current state of generalisation research in NLP, and make
recommendations for the future. Along with this paper, we release a webpage
where the results of our review can be dynamically explored, and which we
intend to up-date as new NLP generalisation studies are published. With this
work, we aim to make steps towards making state-of-the-art generalisation
testing the new status quo in NLP.Comment: 35 pages of content + 53 pages of reference
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