Chao Zhao
2021
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
Faeze Brahman
|
Meng Huang
|
Oyvind Tafjord
|
Chao Zhao
|
Mrinmaya Sachan
|
Snigdha Chaturvedi
Findings of the Association for Computational Linguistics: EMNLP 2021
When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU – a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
2020
Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation
Chao Zhao
|
Marilyn Walker
|
Snigdha Chaturvedi
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Generating sequential natural language descriptions from graph-structured data (e.g., knowledge graph) is challenging, partly because of the structural differences between the input graph and the output text. Hence, popular sequence-to-sequence models, which require serialized input, are not a natural fit for this task. Graph neural networks, on the other hand, can better encode the input graph but broaden the structural gap between the encoder and decoder, making faithful generation difficult. To narrow this gap, we propose DualEnc, a dual encoding model that can not only incorporate the graph structure, but can also cater to the linear structure of the output text. Empirical comparisons with strong single-encoder baselines demonstrate that dual encoding can significantly improve the quality of the generated text.
Search
Co-authors
- Snigdha Chaturvedi 2
- Faeze Brahman 1
- Meng Huang 1
- Oyvind Tafjord 1
- Mrinmaya Sachan 1
- show all...