| Challenge: | Existing chat dialogue systems only implicitly consider the topic given the context, but not explicitly. |
| Approach: | They propose a dialogue system that responds appropriately following the topic by selecting the entity with the highest “topicality” they define the entity as a noun or compound nouns, and topicality as the degree of speaker awareness directed toward each entity in the dialogue context. |
| Outcome: | The proposed system can follow the topic more than existing systems that only consider the context . |
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| Challenge: | Past work has focused on word frequency-based approaches to improving specificity, such as penalizing responses with only common words. |
| Approach: | They propose to rerank a sequence-to-sequence model to improve the informativeness, reasonableness, and grammatically of responses by using externally-trained classifiers targeting each of these factors. |
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Learning to Control the Specificity in Neural Response Generation (P18-1)
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| Challenge: | Existing generative conversational models tend to favor general and trivial responses which appear frequently. |
| Approach: | They propose a controlled response generation mechanism to handle different utterance-response relationships in terms of specificity. |
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Consistent Response Generation with Controlled Specificity (2020.findings-emnlp)
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| Challenge: | Existing methods to generate fluent responses generate inconsistent responses . we use a sequence-to-sequence model to generate specific responses based on a co-occurrence degree . |
| Approach: | They propose a method to control the specificity of responses while maintaining the consistency with the utterances. |
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Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations (2024.lrec-main)
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| Challenge: | Personalization is a multifaceted process that requires multiple definitions and varies between individuals. |
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Automatic Dialogue Generation with Expressed Emotions (N18-2)
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| Challenge: | a growing interest in neural dialogue generation systems is focusing on generating human-like responses based on past utterances . despite efforts, few consider putting restrictions on the response itself . authors present three models that concatenate the desired emotion with the source input . |
| Approach: | They propose three models that concatenate the desired emotion with the source input or push the emotion in the decoder. |
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PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable (2020.acl-main)
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| Challenge: | Existing pre-training models for dialogue generation have been proven effective for a wide range of tasks. |
| Approach: | They propose a dialogue generation pre-training framework that leverages bi-directional context and uni-directional characteristic of language generation. |
| Outcome: | The proposed framework is superior to existing models on three publicly available datasets. |
Adaptive Parameterization for Neural Dialogue Generation (D19-1)
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| Challenge: | Existing models of open-domain dialogue generate responses based on sequence-to-sequence paradigms. |
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Neural Generation of Dialogue Response Timings (2020.acl-main)
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| Challenge: | Using neural models, the timings of spoken response offsets in human dialogue can vary based on contextual elements of the dialogue. |
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Topic-relevant Response Generation using Optimal Transport for an Open-domain Dialog System (2020.coling-main)
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| Challenge: | Conventional neural generative models generate safe and generic responses which have little connection with previous utterances semantically and would disengage users in a dialog system. |
| Approach: | They propose a method that employs topical constraint and semantic constraint to generate relevant responses by regularizing the decoding objective function with semantic distance. |
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Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
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