Diverse dialogue generation by fusing mutual persona-aware and self-transferrer

Xu, Fuyong; Xu, Guangtao; Wang, Yuanying; Ru, Wang; Ding, Qi; Liu, Peiyu; Zhu, Zhenfang

doi:10.1007/s10489-021-02660-4

Cited by 17 publications

(5 citation statements)

References 27 publications

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“…However, the inherent limitation of input text in providing sufficient knowledge poses challenges for neural generation models to achieve the desired output quality [25][26][27]. Many research efforts have been made to enhance the control of generation with various desired properties, such as topic [28], emotion [29], keywords [30], dialogue intent [31], etc. In particular, narrative generation requires models to produce fluent and logically coherent stories based on predefined conditions [32][33][34].…”

Section: Knowledge-enhanced Text Generationmentioning

confidence: 99%

CLICK: Integrating Causal Inference and Commonsense Knowledge Incorporation for Counterfactual Story Generation

Li,

Guo,

Liu

et al. 2023

Electronics

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Counterfactual reasoning explores what could have happened if the circumstances were different from what actually occurred. As a crucial subtask, counterfactual story generation integrates counterfactual reasoning into the generative narrative chain, which requires the model to preserve minimal edits and ensure narrative consistency. Previous work prioritizes conflict detection as a first step, and then replaces conflicting content with appropriate words. However, these methods mainly face two challenging issues: (a) the causal relationship between story event sequences is not fully utilized in the conflict detection stage, leading to inaccurate conflict detection, and (b) the absence of proper planning in the content rewriting stage results in a lack of narrative consistency in the generated story ending. In this paper, we propose a novel counterfactual generation framework called CLICK based on causal inference in event sequences and commonsense knowledge incorporation. To address the first issue, we utilize the correlation between adjacent events in the story ending to iteratively calculate the contents from the original ending affected by the condition. The content with the original condition is then effectively prevented from carrying over into the new story ending, thereby avoiding causal conflict with the counterfactual conditions. Considering the second issue, we incorporate structural commonsense knowledge about counterfactual conditions, equipping the framework with comprehensive background information on the potential occurrence of counterfactual conditional events. Through leveraging a rich hierarchical data structure, CLICK gains the ability to establish a more coherent and plausible narrative trajectory for subsequent storytelling. Experimental results show that our model outperforms previous unsupervised state-of-the-art methods and achieves gains of 2.65 in BLEU, 4.42 in ENTScore, and 3.84 in HMean on the TIMETRAVEL dataset.

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Section: Knowledge-enhanced Text Generationmentioning

confidence: 99%

CLICK: Integrating Causal Inference and Commonsense Knowledge Incorporation for Counterfactual Story Generation

Li,

Guo,

Liu

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Recently, many studies have explored different approaches to personalized dialogue generation (Yang et al 2020;Song et al 2020;Zheng et al 2020;Wu et al 2020;Xu et al 2021). P 2 BOT (Liu et al 2020) and TTransfo (Wolf et al 2019b) The aforementioned approaches require additional personal data (i.e., profiles) for inference.…”

Section: Personalized Dialoguementioning

confidence: 99%

A Comprehensive Empirical Study on Personalized Dialogue Generation

Cho,

Wang,

Takahashi

et al. 2023

Journal of Natural Language Processing

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Current studies on the generation of personalized dialogue primarily contribute to an agent presenting a consistent personality and driving a more informative response. However, we found that the responses generated from most previous models were self-centered, with little consideration for the user in the dialogue. Moreover, we consider human-like conversations to be essentially based on inferring information about the persona of the other party. Therefore, we propose a novel personalized dialogue generator that detects implicit user personas. Because it is difficult to collect a large amount of detailed personal facts for each user, we attempted to model the potential persona of a user and its representation from the dialogue history with no external knowledge. The perception and fader variables were conceived using conditional variational inference. The two latent variables simulate the process of people becoming aware of each other's personas and producing a corresponding expression in conversations. Subsequently, posterior-discriminated regularization was performed to enhance the training procedure. Finally, a selector was designed to help our model provide long-sighted responses. Comprehensive experiments demonstrate that compared to the state-of-the-art methods, our approach is more concerned with the user's persona and achieves a notable boost across both automatic metrics and human evaluations.

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“…We perform our experiments on the PersonaChat ConvAI2 dataset [22], which has been used in the past for conversation models [41][42][43]. We generate training and testing data by taking the current utterances and their corresponding context (ignoring the persona information available from ConvAI2).…”

Section: Dataset 41 the Personachat Convai2 Datasetmentioning

confidence: 99%

Contextual Modeling in Context-Aware Conversation Systems

2023

KSII TIIS

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Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.

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Diverse dialogue generation by fusing mutual persona-aware and self-transferrer

Cited by 17 publications

References 27 publications

CLICK: Integrating Causal Inference and Commonsense Knowledge Incorporation for Counterfactual Story Generation

CLICK: Integrating Causal Inference and Commonsense Knowledge Incorporation for Counterfactual Story Generation

A Comprehensive Empirical Study on Personalized Dialogue Generation

Contextual Modeling in Context-Aware Conversation Systems

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