| Challenge: | Using neural models, the timings of spoken response offsets in human dialogue can vary based on contextual elements of the dialogue. |
| Approach: | They propose neural models that simulate the distributions of response offsets taking into account the response turn as well as the preceding turn. |
| Outcome: | The proposed models can generate distributions of response offsets based on the response turn and preceding turn based upon human listening tests and offline experiments. |
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| Challenge: | Existing pipeline approaches for task-oriented dialogue systems tend to predict multiple dialogue acts first and use them to assist response generation. |
| Approach: | They propose a neural co-generation model that generates dialogue acts and responses concurrently and preserves semantic structures of multi-domain dialogue acts. |
| Outcome: | The proposed model improves over state-of-the-art models in automatic and human evaluations on a large-scale dataset. |
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. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
| Outcome: | The proposed model outperforms state-of-the-art methods in evaluation and human judgment. |
Knowledge Diffusion for Neural Dialogue Generation (P18-1)
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| Challenge: | End-to-end neural dialogue generation does not employ knowledge to guide the generation. |
| Approach: | They propose a neural knowledge diffusion model to introduce knowledge into dialogue generation. |
| Outcome: | The proposed model outperforms baseline models on a real-world dataset. |
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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Evaluating Dialogue Generation Systems via Response Selection (2020.acl-main)
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| Challenge: | Existing automatic evaluation metrics for open-domain dialogue systems correlate poorly with human evaluation. |
| Approach: | They propose to construct response selection test sets with well-chosen false candidates to evaluate response generation systems via response selection. |
| Outcome: | The proposed method correlates with human evaluation better than widely used metrics such as BLEU. |
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling (2020.coling-main)
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| Challenge: | Existing models for machine translation and dialogue response generation require a large number of handcrafted features. |
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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. |
Explicit Use of Topicality in Dialogue Response Generation (2022.naacl-srw)
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| 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. |
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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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Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks (2020.emnlp-main)
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| Challenge: | Existing approaches to multi-turn response generation for open-domain dialogues have a complexity problem . auxiliary tasks that relate to context understanding can guide the learning of the generation model . |
| Approach: | They propose a multi-turn response generation model that has a simple structure yet can effectively leverage conversation contexts for response generation. |
| Outcome: | The proposed model outperforms state-of-the-art models in response quality and human judgment . it also enjoys a faster decoding process . |