Multilingual Transfer Learning for QA using Translation as Data Augmentation
Authors: Mihaela Bornea, Lin Pan, Sara Rosenthal, Radu Florian, Avirup Sil12583-12591
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that the proposed models outperform the previous zero-shot baseline on the recently introduced multilingual MLQA and TYDI QA datasets. |
| Researcher Affiliation | Industry | Mihaela Bornea, Lin Pan, Sara Rosenthal, Radu Florian, Avirup Sil IBM Research AI, Thomas J. Watson Research Center, Yorktown Heights, NY 10598 {mabornea,panl,sjrosenthal,raduf,avi}@us.ibm.com |
| Pseudocode | Yes | Algorithm 1 Pseudo-code for adversarial training on the multilingual QA task. ... Algorithm 2 Pseudo-code for our language arbitration framework for the multilingual QA task. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the source code of the described methodology. It mentions using 'IBM Watson Language Translator' but this is a service, not their code release. |
| Open Datasets | Yes | We train our models on the SQu AD v1.1 dataset (details in Table 1). ... TYDI QA: ... train our models on SQu AD v1.1... |
| Dataset Splits | Yes | We perform hyper-parameter selection on the SQu AD and MLQA dev split. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions 'MBERTQA' and 'MBERT' but does not provide specific version numbers for any underlying software libraries or dependencies (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We use 3 10 5 as the learning rate, 384 as maximum sequence length, and a doc stride of 128. Everything except ZS was trained for 1 epoch. ... The discriminator is implemented as a multilayer perceptron with 2 hidden layers and a hidden size of 768 4. |