Papers by Sebastian Goodman
TeaForN: Teacher-Forcing with N-grams (2020.emnlp-main)
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| Challenge: | Existing methods to address exposure bias and lack of differentiability in sequence generation models with teacherforcing have failed to address these issues. |
| Approach: | They propose a method that uses a stack of N decoders to decode along a secondary time axis and allows model-parameter updates based on N prediction steps. |
| Outcome: | Empirically, teaForN boosts generation quality on one Machine Translation benchmark, WMT 2014 English-French, and two News Summarization benchmarks, CNN/Dailymail and Gigaword. |
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning (P18-1)
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| Challenge: | Practical applications of automatic image description systems include leveraging descriptions for image indexing or retrieval, and helping those with visual impairments by transforming visual signals into information that can be communicated via text-to-speech technology. |
| Approach: | They propose to extract and filter image caption annotations from billions of webpages and use them to train models. |
| Outcome: | The proposed model architectures perform better when trained on the Conceptual Captions dataset. |