Emotion-infused deep neural network for emotionally resonant conversation
The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain kno...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chang, Yung-Chun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Atomic collapse in graphene quantum dots in a magnetic field - Eren, I. ELSEVIER, 2022, the official journal of the World Federation on Soft Computing (WFSC), Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:113 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.asoc.2021.107861 |
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| 520 | |a The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. | ||
| 520 | |a The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. | ||
| 650 | 7 | |a Emotional dimensions |2 Elsevier | |
| 650 | 7 | |a Dialog system |2 Elsevier | |
| 650 | 7 | |a Sentiment analysis |2 Elsevier | |
| 650 | 7 | |a Dialog emotion recognition |2 Elsevier | |
| 650 | 7 | |a Chinese emotional conversation |2 Elsevier | |
| 650 | 7 | |a Natural language processing |2 Elsevier | |
| 700 | 1 | |a Hsing, Yan-Chun |4 oth | |
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10.1016/j.asoc.2021.107861 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001605.pica (DE-627)ELV056100892 (ELSEVIER)S1568-4946(21)00783-3 DE-627 ger DE-627 rakwb eng 540 530 VZ 33.00 bkl Chang, Yung-Chun verfasserin aut Emotion-infused deep neural network for emotionally resonant conversation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. Emotional dimensions Elsevier Dialog system Elsevier Sentiment analysis Elsevier Dialog emotion recognition Elsevier Chinese emotional conversation Elsevier Natural language processing Elsevier Hsing, Yan-Chun oth Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:113 year:2021 pages:0 https://doi.org/10.1016/j.asoc.2021.107861 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 113 2021 0 |
| spelling |
10.1016/j.asoc.2021.107861 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001605.pica (DE-627)ELV056100892 (ELSEVIER)S1568-4946(21)00783-3 DE-627 ger DE-627 rakwb eng 540 530 VZ 33.00 bkl Chang, Yung-Chun verfasserin aut Emotion-infused deep neural network for emotionally resonant conversation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. Emotional dimensions Elsevier Dialog system Elsevier Sentiment analysis Elsevier Dialog emotion recognition Elsevier Chinese emotional conversation Elsevier Natural language processing Elsevier Hsing, Yan-Chun oth Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:113 year:2021 pages:0 https://doi.org/10.1016/j.asoc.2021.107861 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 113 2021 0 |
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10.1016/j.asoc.2021.107861 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001605.pica (DE-627)ELV056100892 (ELSEVIER)S1568-4946(21)00783-3 DE-627 ger DE-627 rakwb eng 540 530 VZ 33.00 bkl Chang, Yung-Chun verfasserin aut Emotion-infused deep neural network for emotionally resonant conversation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. Emotional dimensions Elsevier Dialog system Elsevier Sentiment analysis Elsevier Dialog emotion recognition Elsevier Chinese emotional conversation Elsevier Natural language processing Elsevier Hsing, Yan-Chun oth Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:113 year:2021 pages:0 https://doi.org/10.1016/j.asoc.2021.107861 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 113 2021 0 |
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10.1016/j.asoc.2021.107861 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001605.pica (DE-627)ELV056100892 (ELSEVIER)S1568-4946(21)00783-3 DE-627 ger DE-627 rakwb eng 540 530 VZ 33.00 bkl Chang, Yung-Chun verfasserin aut Emotion-infused deep neural network for emotionally resonant conversation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. Emotional dimensions Elsevier Dialog system Elsevier Sentiment analysis Elsevier Dialog emotion recognition Elsevier Chinese emotional conversation Elsevier Natural language processing Elsevier Hsing, Yan-Chun oth Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:113 year:2021 pages:0 https://doi.org/10.1016/j.asoc.2021.107861 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 113 2021 0 |
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10.1016/j.asoc.2021.107861 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001605.pica (DE-627)ELV056100892 (ELSEVIER)S1568-4946(21)00783-3 DE-627 ger DE-627 rakwb eng 540 530 VZ 33.00 bkl Chang, Yung-Chun verfasserin aut Emotion-infused deep neural network for emotionally resonant conversation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. Emotional dimensions Elsevier Dialog system Elsevier Sentiment analysis Elsevier Dialog emotion recognition Elsevier Chinese emotional conversation Elsevier Natural language processing Elsevier Hsing, Yan-Chun oth Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:113 year:2021 pages:0 https://doi.org/10.1016/j.asoc.2021.107861 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 113 2021 0 |
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The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. |
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The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. |
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The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system. |
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