Monotonic Location Attention for Length Generalization
Jishnu Ray Chowdhury Cornelia Caragea
We explore different ways to utilize position-based cross-attention in
seq2seq networks to enable length generalization in algorithmic tasks. We show
that a simple approach of interpolating the original and reversed encoded
representations combined with relative attention allows near-perfect length
generalization for both forward and reverse lookup tasks or copy tasks that had
been generally hard to tackle. We also devise harder diagnostic tasks where the
relative distance of the ideal attention position varies with timestep. In such
settings, the simple interpolation trick with relative attention is not
sufficient. We introduce novel variants of location attention building on top
of Dubois et al. (2020) to address the new diagnostic tasks. We also show the
benefits of our approaches for length generalization in SCAN (Lake & Baroni,
2018) and CFQ (Keysers et al., 2020). Our code is available on GitHub.