Papers with human-human conversations
Lexical Entrainment for Conversational Systems (2023.findings-emnlp)
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| Challenge: | Conversational agents are expected to possess human-like features such as lexical entrainment (LE). |
| Approach: | They propose a dataset and a measure for LE for conversational systems to explicitly integrate LE into conversational system. |
| Outcome: | The proposed dataset and a measure for LE for conversational systems address this human-like phenomenon. |
The Brain-IHM Dataset: a New Resource for Studying the Brain Basis of Human-Human and Human-Machine Conversations (2020.lrec-1)
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| Challenge: | Using a dataset of controlled interactions, we have studied the feedback items produced by the interlocutors during a conversation. |
| Approach: | They propose to use a dataset of controlled interactions to study feedback items and a virtual reality context to re-synthesize the conversations. |
| Outcome: | The proposed dataset compares human-human and human-machine production of feedbacks and is the first of its kind. |
MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations (2021.eacl-main)
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| Challenge: | Existing dialog act schemes are designed for human-human conversations, but are not suitable for automatic speech recognition. |
| Approach: | They propose a dialog act annotation scheme for open-domain human-machine conversations . they collected 24K utterances from a large open- domain spoken conversation dataset . |
| Outcome: | The proposed scheme achieves an F1 score of 0.79 on a 24K spoken conversation dataset. |
A Unified Approach to Entity-Centric Context Tracking in Social Conversations (2022.lrec-1)
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| Challenge: | Context Tracking is a computational task for human-human conversations . it involves identifying important entities and keeping track of their properties and relationships . |
| Approach: | They propose to use a human-human conversation corpus for context tracking with people and location annotations to model the conversation's context. |
| Outcome: | The proposed model is based on a large human-human conversation corpus with people and location annotations. |
A Dynamic Speaker Model for Conversational Interactions (N19-1)
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| Challenge: | a neural model for characterizing individual differences in speakers is shown to be useful in human-computer interaction and dialog act prediction. |
| Approach: | They propose a neural model for learning a dynamically updated speaker embedding in a conversational context. |
| Outcome: | The proposed model is used for content ranking and dialog act prediction in human-human conversations. |
Ditch the Gold Standard: Re-evaluating Conversational Question Answering (2022.acl-long)
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| Challenge: | Existing conversational question answering systems provide natural-language answers to users in information-seeking conversations. |
| Approach: | They conduct the first large-scale human evaluation of state-of-the-art conversational question answering systems . they propose a question rewriting mechanism based on predicted history which better correlates with human judgments . |
| Outcome: | The proposed question rewriting mechanism better correlates with human judgments. |