June 19, 2020
Measuring what linguistic information is encoded in neural models of language has become popular in NLP. Researchers approach this enterprise by training "probes"—supervised models designed to extract linguistic structure from another model's output. One such probe is the 'structural probe' (Hewitt & Manning 2019), designed to quantify the extent to which syntactic information is encoded in contextualised word representations. The structural probe has a novel design, unattested in the parsing literature, the precise benefit of which is not immediately obvious. To explore whether syntactic probes would do better to make use of existing techniques, we compare the structural probe to a more traditional parser with an identical lightweight parameterisation. The parser outperforms structural probe on UUAS in seven of nine analysed languages, often by a substantial amount (e.g. by 11.1 points in English). Under a second less common metric, however, there is the opposite trend—the structural probe outperforms the parser. This begs the question: which metric should we prefer?
Publisher
ACL
Research Topics
September 05, 2024
Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Luke Zettlemoyer, Omer Levy, Xuezhe Ma
September 05, 2024
August 20, 2024
Ashish Shenoy, Yichao Lu, Srihari Jayakumar, Debojeet Chatterjee, Mohsen Moslehpour, Pierce Chuang, Abhay Harpale, Vikas Bhardwaj, Di Xu (SWE), Shicong Zhao, Ankit Ramchandani, Luna Dong, Anuj Kumar
August 20, 2024
August 11, 2024
Igor Tufanov, Karen Hambardzumyan, Javier Ferrando, Lena Voita
August 11, 2024
August 11, 2024
Marta R. Costa-jussa, Mariano Coria Meglioli, Pierre Andrews, David Dale, Kae Hansanti, Elahe Kalbassi, Christophe Ropers, Carleigh Wood
August 11, 2024
Foundational models
Latest news
Foundational models