Topical-Chat

We introduce Topical-Chat, a knowledge-grounded human-human conversation dataset where the underlying knowledge spans 8 broad topics and conversation partners don’t have explicitly defined roles.

Topical-Chat broadly consists of two types of files:

• Conversations: JSON files containing conversations between pairs of Amazon Mechanical Turk workers.
• Reading Sets: JSON files containing knowledge sections rendered as reading content to the Turkers having conversations.

For detailed information about the dataset, modeling benchmarking experiments and evaluation results, please refer to our paper.

Dataset

Statistics:

Stat	Train	Valid Freq.	Valid Rare	Test Freq.	Test Rare	All
# of conversations	8628	539	539	539	539	10784
# of utterances	188378	11681	11692	11760	11770	235281
average # of turns per conversation	21.8	21.6	21.7	21.8	21.8	21.8
average length of utterance	19.5	19.8	19.8	19.5	19.5	19.6

Split:

The data is split into 5 distinct groups: train, valid frequent, valid rare, test frequent and test rare. The frequent set contains entities frequently seen in the training set. The rare set contains entities that were infrequently seen in the training set.

Configuration Type:

For each conversation to be collected, we applied a random knowledge configuration from a pre-defined list of configurations, to construct a pair of reading sets to be rendered to the partnered Turkers. Configurations were defined to impose varying degrees of knowledge symmetry or asymmetry between partner Turkers, leading to the collection of a wide variety of conversations.

Conversations:

Each JSONL file in conversations/ has the following format:

{
<conversation_id>: {
    “article_url”: <article url>,
    “config”: <config>, # one of A, B, C, D
    “content”: [ # ordered list of conversation turns
        { 
        “agent”: “agent_1”, # or “agent_2”,
        “message” : <message text>,
        “sentiment”: <text>,
        “knowledge_source” : [“AS1”, “Personal Knowledge”, ...],
        “turn_rating”: “Poor”,
        },…
    ],
    “conversation_rating”: {
        “agent_1”: “Good”,
        “agent_2”: “Excellent”
        }
},…
}

• conversation_id: A unique identifier for a conversation in Topical-Chat
• article_url: URL pointing to the Washington Post article associated with a conversation
• config: The knowledge configuration applied to obtain a pair of reading sets for a conversation
• content: An ordered list of conversation turns
  • agent: An identifier for the Turker who generated the message
  • message: The message generated by the agent
  • sentiment: Self-annotation of the sentiment of the message
  • knowledge_source: Self-annotation of the section within the agent's reading set used to generate this message
  • turn_rating: Partner-annotation of the quality of the message
• conversation_rating: Self-annotation of the quality of the conversation
  • agent_1: Rating of the conversation by Turker 1
  • agent_2: Rating of the conversation by Turker 2

- conversation_id: A unique identifier for a conversation in
Topical-Chat
- config: The knowledge configuration applied to obtain a pair of
reading sets for a conversation
- agent_{1/2}: Contains the factual sections in this agent's reading set
    - FS{1/2/3}: Identifier for a factual section
        - entity: A real-world entity
        - shortened_wiki_lead_section: A shortened version of the
        Wikipedia lead section of the entity
        - summarized_wiki_lead_section: A (TextRank) summarized version
        of the Wikipedia lead section of the entity
        - fun_facts: Crowdsourced and manually curated fun facts about
        the entity from Reddit's r/todayilearned subreddit
- article: A Washington Post article common to both partners'
reading sets
    - url: URL pointing to the Washington Post article associated
with a conversation
    - headline: The headline of the Washington Post article
    - AS{1/2/3/4}: A chunk of the body of the Washington Post article

Citation

If you use Topical-Chat in your work, please cite with the following:

@inproceedings{gopalakrishnan2019topical,
  author={Karthik Gopalakrishnan and Behnam Hedayatnia and Qinlang Chen and Anna Gottardi and Sanjeev Kwatra and Anu Venkatesh and Raefer Gabriel and Dilek Hakkani-Tür},
  title={{Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={1891--1895},
  doi={10.21437/Interspeech.2019-3079},
  url={http://dx.doi.org/10.21437/Interspeech.2019-3079}
}

Gopalakrishnan, Karthik, et al. "Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations.", Proc. INTERSPEECH 2019

Acknowledgements

We thank Anju Khatri, Anjali Chadha and Mohammad Shami for their help with the public release of the dataset. We thank Jeff Nunn and Yi Pan for their early contributions to the dataset collection.