Papers by Fangzhou Zhai

4 papers
Script Parsing with Hierarchical Sequence Modelling (2021.starsem-1)

Copied to clipboard

Challenge: Script knowledge is a category of commonsense knowledge that describes how people conduct everyday activities sequentially.
Approach: They propose a hierarchical sequence model and transfer learning to do script parsing with a sequence model that accurately tags script participants.
Outcome: The proposed model improves state of the art of event parsing by over 16 points F-score and, for the first time, accurately tags script participants.
Zero-shot Script Parsing (2022.coling-1)

Copied to clipboard

Challenge: Existing resources cover only a small number of tasks, limiting its practical usefulness.
Approach: They propose a zero-shot learning approach to script parsing which enables us to acquire script knowledge without domain-specific annotations.
Outcome: The proposed model outperforms a previous model with scenario-specific supervision and achieves 68.1/74.4 average F1 for event / participant parsing.
Aligning Actions Across Recipe Graphs (2021.emnlp-main)

Copied to clipboard

Challenge: a recipe explains step by step how to cook a dish, but recipes differ in which cooking actions they describe explicitly, how they describe them, and in which order.
Approach: They propose a recipe corpus which annotates cooking steps in recipes at sentence level . they train a neural model to predict recipes on ARA and model it for automatic understanding .
Outcome: The proposed model can predict recipes with fine-grained structural information . it shows that recipes can be explained in different ways, or not at all .
Story Generation with Rich Details (2020.coling-main)

Copied to clipboard

Challenge: Recent neural story generation systems have been able to produce coherent stories.
Approach: They propose a model that features an outliner, which proceeds the main story line to realize global coherence, and a detailer, which supplies relevant details to the story in a locally coherent manner.
Outcome: The proposed model outperforms baseline models in the informativeness and coherence tests on human participants.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations