Reflect Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality

Data and Code for Paper "Reflect Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality" (EMNLP 2022)

[Project Website] (https://inklab.usc.edu/Reflect/)

[Paper] (https://arxiv.org/abs/2211.09267)

Reflect is a dataset that annotates dialogues with explicit CG (materialized as inferences approximating shared knowledge and beliefs) and solicits diverse human-generated responses each following one common ground.

Reflect contains 9000 diverse responses from 600 dialogue contexts, based on 5 inference dimensions for CG. We collect three responses for each inference dimension, so there are 15 diverse responses for each dialogue context.

Content

• data contains our main dataset (data/organized_Reflect_9k_responses.json) in json file. Each dictionary in the file contains the following keywards:

  • dialogue: the dialogue history where each utterance is separated by <br>;
  • speaker: the speaker name (note that our collected responses and reactions are from the perspective of Friend);
  • reaction_1: the inference answer we collect in stage 1 following the questions "How would you describe Speaker?"
  • reaction_2: the inference answer we collect in stage 1 following the questions "What might have happened before?"
  • reaction_1: the inference answer we collect in stage 1 following the questions "What might happen after?"
  • reaction_1: the inference answer we collect in stage 1 following the questions "What is Speaker feeling now?"
  • reaction_1: the inference answer we collect in stage 1 following the questions "What are you feeling now?"
  • responses_1 to responses_5: responses (3 for each inference dimension) we collect in stage 2 following each of the corresponding inference answer/reaction.
  • utterances: the dialogue history as a list of utterances (the first speaker is always the person in speaker );

• exps contains code we used to fine-tune BlenderBot on Reflect and GPT-3 scripts.

Contact

Feel free to directly email peiz[at]usc[dot]edu if you have any feedback.

Citation

Please cite our EMNLP 2022 paper if you find this data helpful.

@inproceedings{zhou2022reflect,
		title={Reflect Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality},
		author={Zhou, Pei and Cho, Hyundong J. and Jandaghi, Pegah and Lee, Dong-Ho and Lin, Bill Yuchen and Pujara, Jay and Ren, Xiang},
		booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
		year={2022}
	  }