Papers by Abhishek Shah
Multilingual BERT Post-Pretraining Alignment (2021.naacl-main)
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| Challenge: | Recent work improves on the success of monolingual pretrained language models by adding cross-lingual tasks that always involve English. |
| Approach: | They propose a method to align multilingual contextual embeddings as a post-pretraining step for improved cross-lingual transferability of pretrained language models. |
| Outcome: | The proposed model outperforms XLM-R_Base on translation-train tasks while using less parallel data and fewer parameters. |
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations (2021.naacl-industry)
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| Challenge: | Intent detection models require large amounts of labeled data to achieve high accuracy, and in practical scenarios it is more common to find small, unbalanced, and noisy datasets. |
| Approach: | They benchmark intent detection methods on a variety of datasets and found that Watson Assistant's model outperforms other commercial solutions. |
| Outcome: | The proposed model outperforms pretrained language models on a variety of datasets while requiring only a fraction of computational resources and training data. |
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning (P19-1)
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| Challenge: | Abstract meaning representations (AMRs) are labeled directed acyclic graphs that represent a non intersentential abstraction of natural language with broad-coverage semantic representations. |
| Approach: | They build upon a transition-based AMR parser that uses Stack-LSTMs and augment training with policy learning. |
| Outcome: | The proposed parser performs comparable to the best published parsers. |