Ankur Parikh - Towards High Precision Text Generation

From Katie Gentilello November 18th, 2020

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Despite large advances in neural text generation in terms of fluency, existing generation techniques are prone to hallucination and often produce output that is unfaithful or irrelevant to the source text. In this talk, we take a multi-faceted approach to this problem from 3 aspects: data, evaluation, and modeling.

From the data standpoint, we propose ToTTo, a tables-to-text-dataset with high quality annotator revised references that we hope can serve as a benchmark for high precision text generation task. While the dataset is challenging, existing n-gram based evaluation metrics are often insufficient to detect hallucinations. To this end, we propose BLEURT, a fully learnt end-to-end metric based on transfer learning that can quickly adapt to measure specific evaluation criteria. Finally, we propose a model based on confidence decoding to mitigate hallucinations.

Tags: ml@gt seminar seriesmachine learninggeorgia techfall 2020

name: Ankur Parikh
Date: November 11th, 2020
Appears In: Open Scholarship

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Ankur Parikh - Towards High Precision Text Generation

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