Papers by David Vandyke
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. |
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. |
TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles (2024.findings-acl)
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| Challenge: | Existing datasets for Task-Oriented Dialogs (TOD) lack consideration for adaptive response styles and neglect to simulate interactions with app contexts like calendars or alarms. |
| Approach: | They propose to generate an annotated task-oriented dialog dataset and an automatic pipeline to generate it. |
| Outcome: | The proposed dataset provides a variety of system response styles and provides verbose or non-verbal responses. |
A Generative Model for Joint Natural Language Understanding and Generation (2020.acl-main)
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| Challenge: | Natural language understanding (NLU) and natural language generation (NLG) have opposite goals. |
| Approach: | They propose a generative model which couples NLU and NLG through a shared latent variable. |
| Outcome: | The proposed model achieves state-of-the-art performance on two dialogue datasets with flat and tree-structured formal representations. |
Plan-then-Generate: Controlled Data-to-Text Generation via Planning (2021.findings-emnlp)
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| Challenge: | Existing studies focus on producing results that are close to the references, i.e. what to generate and in what order (the output structure) cannot be explicitly controlled by the users. |
| Approach: | They propose a Plan-then-Generate framework to improve the controllability of neural data-to-text models. |
| Outcome: | The proposed model can control both the intra-sentence and inter-sentent structure of the generated output. |