Papers with dialogue research
An Empirical Study on the Overlapping Problem of Open-Domain Dialogue Datasets (2022.lrec-1)
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| Challenge: | Existing benchmark datasets for open-domain dialogue generation are advancing the field . overlapping between training and test sets can cause fake performance . |
| Approach: | They analyze dailyDialog and OpenSubtitles to find out how overlapping can be exploited to obtain fake state-of-the-art performance. |
| Outcome: | The proposed datasets are cleaned and set up for future research. |
Analysis of Sensation-transfer Dialogues in Motorsports (2024.lrec-main)
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| Challenge: | a recent study has examined the effects of subjective ideas on group performance in motorsports. |
| Approach: | They collected dialogues between drivers and engineers in motorsports to test this hypothesis . they defined "sensation" as a unique event unfolding in the mind of a speaker . |
| Outcome: | The results show that the more subjective information interlocutors exchange, the better the group performance in collaborative work. |
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI (2024.findings-eacl)
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Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | DialogStudio is the largest and most diverse collection of dialogue datasets . existing datasets lack diversity and comprehensiveness, authors say . |
| Approach: | They introduce DialogStudio: the largest and most diverse collection of dialogue datasets . DialogStuio aggregates more than 80 diverse dialogue dataset . |
| Outcome: | a new dataset is created to improve the quality and diversity of dialogue datasets . DialogStudio is the largest and most diverse collection of dialogue data . |
A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection (2020.coling-main)
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| Challenge: | Existing models for dialogue breakdown detection do not focus on preventing dialogue breakdowns. |
| Approach: | They propose a model that integrates a pretrained cross-lingual language model and a co-attention network for dialogue breakdown detection. |
| Outcome: | The proposed model outperforms all previous approaches on evaluation metrics in Japanese and English tracks in Dialogue Breakdown Detection Challenge 4 . |
A Large-Scale Corpus for Conversation Disentanglement (P19-1)
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Jonathan K. Kummerfeld, Sai R. Gouravajhala, Joseph J. Peper, Vignesh Athreya, Chulaka Gunasekara, Jatin Ganhotra, Siva Sankalp Patel, Lazaros C Polymenakos, Walter Lasecki
| Challenge: | a dataset of 77,563 messages manually annotated with reply-structure graphs disentangles conversations and defines internal conversation structure. |
| Approach: | They use a dataset of 77,563 messages manually annotated with reply-structure graphs to disentangle conversations and define internal conversation structure. |
| Outcome: | The new dataset is 16 times larger than all previous datasets combined and includes adjudication of annotation disagreements and context. |
Don’t Copy the Teacher: Data and Model Challenges in Embodied Dialogue (2022.emnlp-main)
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| Challenge: | Embodied dialogue instruction following requires an agent to complete a complex sequence of tasks from a natural language exchange. |
| Approach: | They argue that imitation learning and low-level metrics are misleading . they compare existing models with IL and argue evaluation should focus on higher-level semantic goals . |
| Outcome: | The proposed model evaluations are based on three models and compare them with benchmarks . they show that existing models fail to ground query utterances, which are essential for task completion . |