November 16, 2021
Despite their failure to solve the compositional SCAN dataset, seq2seq architectures still achieve astonishing success on more practical tasks. This observation pushes us to question the usefulness of SCAN-style compositional generalization in realistic NLP tasks. In this work, we study the benefit that such compositionality brings about to several machine translation tasks. We present several focused modifications of Transformer that greatly improve generalization capabilities on SCAN and select one that remains on par with a vanilla Transformer on a standard machine translation (MT) task. Next, we study its performance in low-resource settings and on a newly introduced distribution-shifted English-French translation task. Overall, we find that improvements of a SCAN-capable model do not directly transfer to the resource-rich MT setup. In contrast, in the low-resource setup, general modifications lead to an improvement of up to 13.1% BLEU score w.r.t. a vanilla Transformer. Similarly, an improvement of 14% in an accuracy-based metric is achieved in the introduced compositional English-French translation task. This provides experimental evidence that the compositional generalization assessed in SCAN is particularly useful in resource-starved and distribution-shifted scenarios.
Written by
Rahma Chaabouni
Roberto Dessì
Evgeny Kharitonov
Publisher
BlackBox Workshop
Research Topics
May 22, 2023
Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Wei-Ning Hsu, Alexis Conneau, Michael Auli
May 22, 2023
February 24, 2023
Faisal Azhar, Hugo Touvron, Armand Joulin, Aurelien Rodriguez, Baptiste Rozière, Eric Hambro, Gautier Izacard, Guillaume Lample, Marie-Anne Lachaux, Naman Goyal, Thibaut Lavril, Timothee Lacroix, Xavier Martinet, Edouard Grave
February 24, 2023
February 20, 2023
Maziar Sanjabi, Aaron Chan, Hamed Firooz, Lambert Mathias, Liang Tan, Shaoliang Nie, Xiaochang Peng, Xiang Ren
February 20, 2023
December 31, 2022
Yossef Mordechay Adi, Abdelrahman Mohamed, Adam Polyak, Emmanuel Dupoux, Evgeny Kharitonov, Jade Copet, Morgane Rivière, Tu Anh Nguyen, Wei-Ning Hsu, Felix Kreuk
December 31, 2022
Latest Work
Our Actions
Newsletter