Papers with Switchboard Dialog
Human-like informative conversations: Better acknowledgements using conditional mutual information (2021.naacl-main)
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| Challenge: | Existing chatbots generate responses that are non-specific w.r.t. one of the contexts, typically the conversational history. |
| Approach: | They propose to build a dialogue agent that can weave new factual content into conversations as naturally as humans. |
| Outcome: | The proposed method trades off pmi for pcmi_h and is preferred by humans for overall quality over the Max-PMI baseline 60% of the time. |
What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition (2021.tacl-1)
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| Challenge: | Existing punctuation in the transcripts has a massive effect on the models’ performance, and specific label set specificity does not affect dialog act segmentation performance. |
| Approach: | They apply two pre-trained transformer models to a conversation transcript as a sequence of dialog acts and achieve strong results on Switchboard Dialog Act and Meeting Recorder Dialog Act corpora. |
| Outcome: | The proposed models achieve 8.4% and 14.2% error rates on the Switchboard Dialog Act and Meeting Recorder Dialog Act corpora. |
Disentangling Indirect Answers to Yes-No Questions in Real Conversations (2022.naacl-main)
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Krishna Sanagavarapu, Jathin Singaraju, Anusha Kakileti, Anirudh Kaza, Aaron Mathews, Helen Li, Nathan Brito, Eduardo Blanco
| Challenge: | Existing models with synthetic indirect answers to yes-no questions are not beneficial when working with real conversations. |
| Approach: | They propose to annotate the underlying direct answers to yes-no questions in real conversations. |
| Outcome: | The proposed model outperforms the majority baseline but the task remains a challenge. |
The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves (2022.lrec-1)
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| Challenge: | Currently, classroom recordings are limited due to practical and privacy concerns and sharing is restricted due to limited access to valuable resources and data sets. |
| Approach: | They propose to use the TalkMoves dataset to analyze the nature of teacher and student discourse in K-12 math classrooms. |
| Outcome: | The TalkMoves dataset contains 567 human-annotated K-12 mathematics lesson transcripts derived from video recordings. |