alexchern5757/groundhog_reddit

The dataset is presented in the paper GroundHog: Dialogue Generation using Multi-Grained Linguistic Input

GitHub repo: https://github.com/alchernyavskiy/GroundHog

NOTE Some dialogues may have the same beginning. This is due to the fact that in our case, the dialogue is a replica chain, which is built according to the replica tree in the source data.

The dataset is uploaded in .jsonl format as List[Dialogue]

Dialogue:

• dialogue: List[Utterance]
• meta: Meta
• grounding: str
• reddit_id: str

Utterance:

• id: str
• speaker: str
• text: str
• discourse: Triplet[from: str, to: str, relation: str]
• sentiment: Pair[class: str, score: float]
• AMR: str

Meta:

• id: str
• title: str
• score: float
• comms_num: int
• url: str
• created: str

Citation

If you find this dataset helpful, feel free to cite our publication:

@inproceedings{chernyavskiy-etal-2024-groundhog,
    title = "{G}round{H}og: Dialogue Generation using Multi-Grained Linguistic Input",
    author = "Chernyavskiy, Alexander  and
      Ostyakova, Lidiia  and
      Ilvovsky, Dmitry",
    editor = "Strube, Michael  and
      Braud, Chloe  and
      Hardmeier, Christian  and
      Li, Junyi Jessy  and
      Loaiciga, Sharid  and
      Zeldes, Amir  and
      Li, Chuyuan",
    booktitle = "Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)",
    month = mar,
    year = "2024",
    address = "St. Julians, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.codi-1.14",
    pages = "149--160",
    abstract = "Recent language models have significantly boosted conversational AI by enabling fast and cost-effective response generation in dialogue systems. However, dialogue systems based on neural generative approaches often lack truthfulness, reliability, and the ability to analyze the dialogue flow needed for smooth and consistent conversations with users. To address these issues, we introduce GroundHog, a modified BART architecture, to capture long multi-grained inputs gathered from various factual and linguistic sources, such as Abstract Meaning Representation, discourse relations, sentiment, and grounding information. For experiments, we present an automatically collected dataset from Reddit that includes multi-party conversations devoted to movies and TV series. The evaluation encompasses both automatic evaluation metrics and human evaluation. The obtained results demonstrate that using several linguistic inputs has the potential to enhance dialogue consistency, meaningfulness, and overall generation quality, even for automatically annotated data. We also provide an analysis that highlights the importance of individual linguistic features in interpreting the observed enhancements.",
}