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Understanding and characterizing how people interact in information-seeking conversations will be a crucial component in developing effective conversational search systems. In this paper, we introduce a new dataset designed for this purpose and use it to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns. The MSDialog dataset is a labeled conversation dataset of question answering (QA) interactions between information seekers and providers from an online forum on Microsoft products. The dataset contains more than 2,000 multi-turn QA dialogs with 10,000 utterances that are annotated with user intents on the utterance level. Annotations were done using crowdsourcing. With MSDialog, we find some highly recurring patterns in user intent during an information-seeking process. They could be useful for designing conversational search systems. We will make our dataset freely available to encourage exploration of information-seeking conversation models.
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Conversation Modeling to Predict Derailment
Conversations among online users sometimes derail, i.e., break down into personal attacks. Derailment interferes with the healthy growth of communities in cyberspace. The ability to predict whether an ongoing conversation will derail could provide valuable advance, even real-time, insight to both interlocutors and moderators. Prior approaches predict conversation derailment retrospectively without the ability to forestall the derailment proactively. Some existing works attempt to make dynamic predictions as the conversation develops, but fail to incorporate multisource information, such as conversational structure and distance to derailment. We propose a hierarchical transformer-based framework that combines utterance-level and conversation-level information to capture fine-grained contextual semantics. We propose a domain-adaptive pretraining objective to unite conversational structure information and a multitask learning scheme to leverage the distance from each utterance to derailment. An evaluation of our framework on two conversation derailment datasets shows an improvement in F1 score for the prediction of derailment. These results demonstrate the effectiveness of incorporating multisource information for predicting the derailment of a conversation.
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- Award ID(s):
- 2116751
- PAR ID:
- 10454933
- Date Published:
- Journal Name:
- Proceedings of the International AAAI Conference on Web and Social Media
- Volume:
- 17
- ISSN:
- 2162-3449
- Page Range / eLocation ID:
- 926 to 935
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/icwsm.v17i1.22200
