Papers with end-to-end neural methods
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models (2022.findings-emnlp)
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| Challenge: | Existing approaches to data-to-text generation require limited training examples . a data-based approach is based on a set of pre-trained language models with optional finetuning. |
| Approach: | They propose a data-to-text generation task that makes use of any given (or no) examples. |
| Outcome: | The proposed approach improves on baselines on a dataset with zero/few/full-shot settings. |
Effects of Naturalistic Variation in Goal-Oriented Dialog (2020.findings-emnlp)
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| Challenge: | Existing benchmarks for end-to-end neural dialog systems lack a key component: natural variation. |
| Approach: | They propose new and more effective testbeds by introducing naturalistic variation by the user. |
| Outcome: | The proposed testbeds incorporate natural variation by the user. |
Learning End-to-End Goal-Oriented Dialog with Multiple Answers (D18-1)
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| Challenge: | Existing methods for dialog learning assume there is only one correct next utterance . a significant drop in performance is seen in existing methods for evaluating dialog systems . |
| Approach: | They propose a method that assumes there is only one correct next utterance in a dialog . they propose bAbI dialog tasks that introduce valid next . |
| Outcome: | The proposed method improves performance and achieves 47.3% accuracy on permuted-bAbI dialog tasks. |