How Do We Answer Complex Questions: Discourse Structure of Long-form Answers

Fangyuan Xu, Junyi Jessy Li, Eunsol Choi.

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About

Long-form answers, consisting of multiple sentences, can provide nuanced and comprehensive answers to a broader set of questions. To better understand this complex and understudied task, we study the functional structure of long-form answers collected from three datasets, ELI5, WebGPT and Natural Questions.
We develop an ontology of six sentence-level functional roles for long-form answers, and annotate 3.9k sentences in 640 answer paragraphs (See figure below for examples of answer paragraphs annotated with sentence-level roles). We further analyze model-generated answers -- finding that annotators agree less with each other when annotating model-generated answers compared to annotating human-written answers.

intro figure

Citations

If you find our work helpful, please cite us as:

@inproceedings{xu2022lfqadiscourse,
    title     = {How Do We Answer Complex Questions: Discourse Structure of Long-form Answers},
    author    = {Xu, Fangyuan and Li, Junyi Jessy and Choi, Eunsol},
    year      = 2022,
    booktitle = {Proceedings of the Annual Meeting of the Association for Computational Linguistics},
    note      = {Long paper}
}

Contact

For any questions, please contact Fangyuan Xu.