Papers by Ohad Rubin
SmBoP: Semi-autoregressive Bottom-up Semantic Parsing (2021.naacl-main)
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| Challenge: | Existing semantic parsers decode syntax using a top-down depth-first traversal. |
| Approach: | They propose a semi-autoregressive bottom-up parser that constructs at decoding step t the top-K sub-trees of height t. |
| Outcome: | The proposed method achieves 2.2x speed-up in decoding time and 5x speed up in training time on a zero-shot semantic parsing benchmark. |
Learning To Retrieve Prompts for In-Context Learning (2022.naacl-main)
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| Challenge: | In-context learning is a new paradigm in natural language understanding . large pre-trained language models can be expensive to update . |
| Approach: | They propose an efficient method for retrieving training examples as prompts from annotated data and an LM. |
| Outcome: | The proposed method outperforms prior work and multiple baselines on three sequence-to-sequence tasks. |