Filip Radlinski


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Proceedings of the First Workshop on Interactive Learning for Natural Language Processing
Kianté Brantley | Soham Dan | Iryna Gurevych | Ji-Ung Lee | Filip Radlinski | Hinrich Schütze | Edwin Simpson | Lili Yu
Proceedings of the First Workshop on Interactive Learning for Natural Language Processing


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I’d rather just go to bed”: Understanding Indirect Answers
Annie Louis | Dan Roth | Filip Radlinski
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We revisit a pragmatic inference problem in dialog: Understanding indirect responses to questions. Humans can interpret ‘I’m starving.’ in response to ‘Hungry?’, even without direct cue words such as ‘yes’ and ‘no’. In dialog systems, allowing natural responses rather than closed vocabularies would be similarly beneficial. However, today’s systems are only as sensitive to these pragmatic moves as their language model allows. We create and release the first large-scale English language corpus ‘Circa’ with 34,268 (polar question, indirect answer) pairs to enable progress on this task. The data was collected via elaborate crowdsourcing, and contains utterances with yes/no meaning, as well as uncertain, middle-ground, and conditional responses. We also present BERT-based neural models to predict such categories for a question-answer pair. We find that while transfer learning from entailment works reasonably, performance is not yet sufficient for robust dialog. Our models reach 82-88% accuracy for a 4-class distinction, and 74-85% for 6 classes.


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Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences
Filip Radlinski | Krisztian Balog | Bill Byrne | Karthik Krishnamoorthi
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

Conversational recommendation has recently attracted significant attention. As systems must understand users’ preferences, training them has called for conversational corpora, typically derived from task-oriented conversations. We observe that such corpora often do not reflect how people naturally describe preferences. We present a new approach to obtaining user preferences in dialogue: Coached Conversational Preference Elicitation. It allows collection of natural yet structured conversational preferences. Studying the dialogues in one domain, we present a brief quantitative analysis of how people describe movie preferences at scale. Demonstrating the methodology, we release the CCPE-M dataset to the community with over 500 movie preference dialogues expressing over 10,000 preferences.