Papers with conversation modeling
Representation Learning for Conversational Data using Discourse Mutual Information Maximization (2022.naacl-main)
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Bishal Santra, Sumegh Roychowdhury, Aishik Mandal, Vasu Gurram, Atharva Naik, Manish Gupta, Pawan Goyal
| Challenge: | Existing language modeling pretraining objectives do not take structural information of conversational text into account. |
| Approach: | They propose a structure-aware Mutual Information based loss-function DMI for training dialog-representation models that captures the inherent uncertainty in response prediction. |
| Outcome: | The proposed model outperforms strong baseline models on nine diverse tasks. |
A Hierarchical Latent Structure for Variational Conversation Modeling (N18-1)
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| Challenge: | Variational autoencoders suffer from the notorious degeneration problem, according to a new study . utterance drop regularization is an important feature of the hierarchical RNNs . |
| Approach: | They propose a variational hierarchical conversation RNN framework that exploits latent variables and an utterance drop regularization to exploit latent variable. |
| Outcome: | The proposed model outperforms state-of-the-art models on Cornell Movie Dialog and Ubuntu Dialog Corpus. |
A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection (D19-1)
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| Challenge: | Dialogue acts are important in conversation modeling, but they are rarely available for new conversations. |
| Approach: | They propose an end-to-end multi-task model that integrates dialogue acts with context and response in a crossway fashion. |
| Outcome: | The proposed model improves the accuracy of the dialogue act prediction task and the MRR for the response selection task. |
QSTS: A Question-Sensitive Text Similarity Measure for Question Generation (2022.coling-1)
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| Challenge: | Existing measures for question generation have been inadequately evaluated . current research uses QA datasets containing pairs of (reference question, passage context) elements. |
| Approach: | They propose a Question-Sensitive Text Similarity measure for comparing two questions . they also propose enabling question similarity research in QG contexts by using a dataset called SimQG. |
| Outcome: | The proposed measure overcomes shortcomings of existing measures that depend on n-gram overlap scores and obtains superior results compared to existing measures on publicly-available QG datasets. |