Neural Dialogue Generation Methods in Open Domain: A Survey

Sun, Bin; Li, Kan

doi:10.2991/nlpr.d.210223.001

Cited by 6 publications

(2 citation statements)

References 46 publications

(63 reference statements)

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“…To achieve a real natural dialog with emotion in HCI, emotion processing and recognition are needed [102][103][104]. Li et al (2019) [105] proposed a method of combining prosody valence and text emotion through decision-level fusion, which reduced fatal recognition errors and thus improved user experience.…”

Section: Natural Language Processing and Hcimentioning

confidence: 99%

Deep Learning for Intelligent Human–Computer Interaction

Poiesi

Dong

et al. 2022

Applied Sciences

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In recent years, gesture recognition and speech recognition, as important input methods in Human–Computer Interaction (HCI), have been widely used in the field of virtual reality. In particular, with the rapid development of deep learning, artificial intelligence, and other computer technologies, gesture recognition and speech recognition have achieved breakthrough research progress. The search platform used in this work is mainly the Google Academic and literature database Web of Science. According to the keywords related to HCI and deep learning, such as “intelligent HCI”, “speech recognition”, “gesture recognition”, and “natural language processing”, nearly 1000 studies were selected. Then, nearly 500 studies of research methods were selected and 100 studies were finally selected as the research content of this work after five years (2019–2022) of year screening. First, the current situation of the HCI intelligent system is analyzed, the realization of gesture interaction and voice interaction in HCI is summarized, and the advantages brought by deep learning are selected for research. Then, the core concepts of gesture interaction are introduced and the progress of gesture recognition and speech recognition interaction is analyzed. Furthermore, the representative applications of gesture recognition and speech recognition interaction are described. Finally, the current HCI in the direction of natural language processing is investigated. The results show that the combination of intelligent HCI and deep learning is deeply applied in gesture recognition, speech recognition, emotion recognition, and intelligent robot direction. A wide variety of recognition methods were proposed in related research fields and verified by experiments. Compared with interactive methods without deep learning, high recognition accuracy was achieved. In Human–Machine Interfaces (HMIs) with voice support, context plays an important role in improving user interfaces. Whether it is voice search, mobile communication, or children’s speech recognition, HCI combined with deep learning can maintain better robustness. The combination of convolutional neural networks and long short-term memory networks can greatly improve the accuracy and precision of action recognition. Therefore, in the future, the application field of HCI will involve more industries and greater prospects are expected.

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Section: Natural Language Processing and Hcimentioning

confidence: 99%

Deep Learning for Intelligent Human–Computer Interaction

Poiesi

Dong

et al. 2022

Applied Sciences

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“…Its development aims towards building a human like conversation system where responses are generated automatically. Dialogue generation systems evolved in three phases which included rule based systems, retrieval based systems and neural generative conversation systems [99]. The quality of the generation system depends upon the intelligence of Manuscript submitted to ACM the system as the generated response should align with the previous statement, i.e.…”

Section: Dialogue Understandingmentioning

confidence: 99%

A survey in Adversarial Defences and Robustness in NLP

Goyal¹,

Doddapaneni²,

Khapra³

et al. 2022

Preprint

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In recent years, it has been seen that deep neural networks are lacking robustness and are likely to break in case of adversarial perturbations in input data. Strong adversarial attacks are proposed by various authors for computer vision and Natural Language Processing (NLP). As a counter-effort, several defense mechanisms are also proposed to save these networks from failing. In contrast with image data, generating adversarial attacks and defending these models is not easy in NLP because of the discrete nature of the text data. However, numerous methods for adversarial defense are proposed of late, for different NLP tasks such as text classification, named entity recognition, natural language inferencing, etc. These methods are not just used for defending neural networks from adversarial attacks, but also used as a regularization mechanism during training, saving the model from overfitting. The proposed survey is an attempt to review different methods proposed for adversarial defenses in NLP in the recent past by proposing a novel taxonomy. This survey also highlights the fragility of the advanced deep neural networks in NLP and the challenges in defending them.

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Implicit Perception of Differences between NLP‐Produced and Human‐Produced Language in the Mentalizing Network

et al. 2023

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Natural language processing (NLP) is central to the communication with machines and among ourselves, and NLP research field has long sought to produce human‐quality language. Identification of informative criteria for measuring NLP‐produced language quality will support development of ever‐better NLP tools. The authors hypothesize that mentalizing network neural activity may be used to distinguish NLP‐produced language from human‐produced language, even for cases where human judges cannot subjectively distinguish the language source. Using the social chatbots Google Meena in English and Microsoft XiaoIce in Chinese to generate NLP‐produced language, behavioral tests which reveal that variance of personality perceived from chatbot chats is larger than for human chats are conducted, suggesting that chatbot language usage patterns are not stable. Using an identity rating task with functional magnetic resonance imaging, neuroimaging analyses which reveal distinct patterns of brain activity in the mentalizing network including the DMPFC and rTPJ in response to chatbot versus human chats that cannot be distinguished subjectively are conducted. This study illustrates a promising empirical basis for measuring the quality of NLP‐produced language: adding a judge's implicit perception as an additional criterion.

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Neural Dialogue Generation Methods in Open Domain: A Survey

Cited by 6 publications

References 46 publications

Deep Learning for Intelligent Human–Computer Interaction

Deep Learning for Intelligent Human–Computer Interaction

A survey in Adversarial Defences and Robustness in NLP

Implicit Perception of Differences between NLP‐Produced and Human‐Produced Language in the Mentalizing Network

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