# The Fine Line between Linguistic Generalization and Failure in Seq2Seq-Attention Models

arXiv: https://arxiv.org/abs/1805.01445 · HTML: https://mlsanity.com/paper/1805.01445/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2018.

## Built on by (most cited first, 4 of 4)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [How Do Neural Sequence Models Generalize? Local and Global Context Cues for Out-of-Distribution Prediction](https://mlsanity.com/paper/2111.03108/) (2021)
- [Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation](https://mlsanity.com/paper/2201.01845/) (2022)
- [Towards Disentangled Speech Representations](https://mlsanity.com/paper/2208.13191/) (2022)

## Abstract (arXiv)

Seq2Seq based neural architectures have become the go-to architecture to apply to sequence to sequence language tasks. Despite their excellent performance on these tasks, recent work has noted that these models usually do not fully capture the linguistic structure required to generalize beyond the dense sections of the data distribution citeettinger2017towards, and as such, are likely to fail on samples from the tail end of the distribution (such as inputs that are noisy citepbelkinovnmtbreak or of different lengths citepbentivoglinmtlength). In this paper, we look at a model's ability to generalize on a simple symbol rewriting task with a clearly defined structure. We find that the model's ability to generalize this structure beyond the training distribution depends greatly on the chosen random seed, even when performance on the standard test set remains the same. This suggests that a model's ability to capture generalizable structure is highly sensitive. Moreover, this sensitivity may not be apparent when evaluating it on standard test sets.