| Challenge: | a neural network estimation system for spoken dialogues can be used to estimate the communication style of a user's interaction, but this is rarely implemented in a live system. |
| Approach: | They propose a neural network approach to estimate the communication style of spoken interaction, namely elaborateness and directness. |
| Outcome: | The proposed method can estimate the elaborateness and directness of spoken interaction and improve the results with additional linguistic features. |
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| Challenge: | Using a multi-cultural approach, we investigated the differences in the communication styles elaborateness and directness of human-computer interaction. |
| Approach: | They propose to design a Spoken Dialogue System which adapts to the user's communication idiosyncrasies and to examine the influence of the user culture and gender on the system's elaborateness and directness. |
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Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models (L18-1)
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| Challenge: | Existing methods for speaker modeling are based on hand-crafted statistics and ad hoc to a certain application. |
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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. |
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An LLM Feature-based Framework for Dialogue Constructiveness Assessment (2024.emnlp-main)
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| Challenge: | Existing studies on dialogue constructiveness assessment focus on analysing conversational factors that influence individuals to take specific actions, win debates, change their perspectives or broaden their open-mindedness. |
| Approach: | They propose an LLM feature-based framework for dialogue constructiveness assessment that combines the strengths of feature- and neural approaches while mitigating their downsides. |
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Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversations (2024.acl-long)
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| Challenge: | In spoken dialogue, even if two current turns are the same sentence, their responses might differ when they are spoken in different styles. |
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DIRECT: Direct and Indirect Responses in Conversational Text Corpus (2021.findings-emnlp)
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| Challenge: | Neural conversation models have been able to generate fluent responses through training on a dialogue corpus, but they lack the ability to reveal the implied intentions of users. |
| Approach: | They propose to train neural conversation models on a dialogue corpus that provides pragmatic paraphrases to advance techniques for natural language understanding in dialogue systems. |
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Language Model Transformers as Evaluators for Open-domain Dialogues (2020.coling-main)
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| Challenge: | Computer-based systems for communication with humans are a cornerstone of AI research since the 1950s. |
| Approach: | They propose to use transformer neural networks to predict one or more words based on an already given context to provide an efficient, automatic indication of dialogue quality. |
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Predicting pragmatic discourse features in the language of adults with autism spectrum disorder (2021.acl-srw)
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| Challenge: | Existing tools to quantify atypicality in discourse and pragmatics are difficult to precisely identify and quantify. |
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Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues (N19-1)
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| Challenge: | Recent surge of text-based online counseling applications enables us to collect and analyze interactions between counselors and clients. |
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Towards Comprehensive Language Analysis for Clinically Enriched Spontaneous Dialogue (2024.lrec-main)
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| Challenge: | Contemporary NLP has progressed from feature-based classification to fine-tuning and prompt-based techniques . many of these techniques remain understudied in the context of real-world, clinically enriched spontaneous dialogue. |
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