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I like fish 🐟, especially dolphins 🐬: Addressing Contradictions in Dialogue Modeling

A study on contradiction detection and non-contradiction generation in dialogue modeling.
The paper can be found here: Nie et al. (2020).

Abstract

To quantify how well natural language understanding models can capture consistency in a general conversation, we introduce the DialoguE COntradiction DEtection task (DECODE) and a new conversational dataset containing both human-human and human-bot contradictory dialogues. We then compare a structured utterance-based approach of using pre-trained Transformer models for contradiction detection with the typical unstructured approach.

Results reveal that:

  1. Our newly collected dataset is notably more effective at providing supervision for the dialogue contradiction detection task than existing NLI data including those aimed to cover the dialogue domain;
  2. The structured utterance-based approach is more robust and transferable on both analysis and out-of-distribution dialogues than its unstructured counterpart.

We also show that our best contradiction detection model correlates well with human judgements and further provide evidence for its usage in both automatically evaluating and improving the consistency of state-of-the-art generative chatbots.

Data

As described in the paper, DECODE includes 6 groups of dialogues: (Main) Train, (Main) Dev, (Main) Test, Human-Bot, A2T, RCT.

Group Name Count Description
Train 27,184 Human-written dialogues
Dev 4,026 Human-written dialogues
Test 4,216 Human-written dialogues
Human-Bot 764 Human-Bot interaction dialogues
A2T 2,079 Auxiliary test set created by transforming examples in Test
RCT 2,011 Auxiliary test set created by transforming examples in Test

The details of each group can be found in the Nie et al. (2020).

Load Data from ParlAI

The DECODE can be loaded directly from ParlAI. The correct arguments to load data belonging to the different subsets is given below.

parlai display_data -t decode -dt train -v                          # Train
parlai display_data -t decode -dt valid -v                          # Dev
parlai display_data -t decode -dt test --test_type vanilla -v       # Test
parlai display_data -t decode -dt test --test_type human-bot -v     # Human-Bot
parlai display_data -t decode -dt test --test_type a2t -v           # A2T
parlai display_data -t decode -dt test --test_type rct -v           # RCT

Directly Download Data.

You can also download the data directly from s3. See download data from s3 with raw format.

Citation

If you use the dataset or models in your own work, please cite with the following BibTex entry:

@misc{nie2020i,
      title={I like fish, especially dolphins: Addressing Contradictions in Dialogue Modelling}, 
      author={Yixin Nie and Mary Williamson and Mohit Bansal and Douwe Kiela and Jason Weston},
      year={2020},
      eprint={2012.13391},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Dolphins 🐬 are mammals, not fish 🐟.