Papers by Simon Baker
Non-Autoregressive Text Generation with Pre-trained Language Models (2021.eacl-main)
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| Challenge: | Autoregressive generation models generate tokens in a left-to-right, token-by-token fashion, resulting in lag in inference. |
| Approach: | They propose to use BERT as the backbone of a non-autoregressive generation model for greatly improved performance. |
| Outcome: | The proposed model outperforms existing non-autoregressive models and achieves competitive performance with many strong autoregressive model. |
Few-Shot Table-to-Text Generation with Prototype Memory (2021.findings-emnlp)
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| Challenge: | Neural table-to-text generation models are data-hungry and require large amounts of training data to learn the mapping between tables and texts. |
| Approach: | They propose a framework for table-to-text generation under the few-shot scenario that uses retrieved prototypes and a prototype selector to bridge the structural gap between tables and texts. |
| Outcome: | The proposed framework significantly improves the model performance on three benchmark datasets with state-of-the-art models. |
Keep the Primary, Rewrite the Secondary: A Two-Stage Approach for Paraphrase Generation (2021.findings-acl)
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| Challenge: | Existing approaches to generate paraphrases are decomposable, but some use a sequence-to-sequence model to generate each word in a uniform way. |
| Approach: | They propose a framework for identification then aggregation of input tokens and a custom decoder to generate paraphrases. |
| Outcome: | The proposed framework outperforms previous studies on two benchmark datasets and generates paraphrases in interpretable and controllable way. |
Variable Typing: Assigning Meaning to Variables in Mathematical Text (N18-1)
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| Challenge: | Scientific documents rely on mathematics to communicate ideas and results . textual contexts are strong domainspecific conventions governing how content is presented . |
| Approach: | They introduce a task of assigning one mathematical type to each variable in a sentence . they also introduce 'variable typing' task that focuses on assignment of meaning to variables . |
| Outcome: | The proposed model is the best performing model on an extrinsic task, the authors show . their model is compared to a formula index only containing raw symbols . |
Dialogue Response Selection with Hierarchical Curriculum Learning (2021.acl-long)
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Yixuan Su, Deng Cai, Qingyu Zhou, Zibo Lin, Simon Baker, Yunbo Cao, Shuming Shi, Nigel Collier, Yan Wang
| Challenge: | Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics. |
| Approach: | They propose a hierarchical curriculum learning framework that trains matching models in an “easy-to-difficult” scheme. |
| Outcome: | The proposed framework significantly improves the model performance across evaluation metrics on three benchmark datasets with three state-of-the-art matching models. |