Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net? (2020.lrec-1)
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| Challenge: | End-to-end neural network models of conversational dialogue are popular for conversational tasks, but there are still questions about how well they work for real applications and how much data is needed to achieve acceptable performance. |
| Approach: | They compare two different kinds of end-to-end dialogue models based on cross-language relevance and cross-linguistic LSTM models for corpus-based selection of dialogue responses. |
| Outcome: | The proposed models perform well on a large corpus, but are dominated by a more moderate-sized corpus. |
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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. |
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| Challenge: | Existing neural dialogue models only capture syntactic and semantic information, but fail to model the logical consistency between the dialogue history and the generated response. |
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An Investigation of Suitability of Pre-Trained Language Models for Dialogue Generation – Avoiding Discrepancies (2021.findings-acl)
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| Challenge: | Pre-trained language models have been widely used in open-domain dialogue generation. |
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Training Neural Response Selection for Task-Oriented Dialogue Systems (P19-1)
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Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su
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SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation (2024.findings-acl)
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| Challenge: | Existing approaches to evaluate open domain dialogues have a one-to-many problem . existing approaches lack commonsense reasoning biases and perform poorly in domain-specific scenarios. |
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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. |
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