Improving Open-Domain Dialogue Response Generation with Multi-Source Multilingual Commonsense Knowledge Article Swipe
Sixing Wu
,
Jiong Yu
,
Jiahao Chen
,
Xiaofan Deng
,
Wei Zhou
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v38i17.29894
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v38i17.29894
Knowledge-grounded Dialogue Response Generation (KRG) can facilitate informative and fidelity dialogues using external knowledge. Prior monolingual works can only use the knowledge of the corresponding native language. Thus, due to the prohibitive costs of collecting and constructing external knowledge bases, the limited scale of accessible external knowledge always constrains the ability of KRG, especially in low-resource language scenarios. To this end, we propose a new task, Multi-Source Multilingual Knowledge-Grounded Response Generation (MMKRG), which simultaneously uses multiple knowledge sources of different languages. We notice that simply combining knowledge of different languages is inefficient due to the Cross-Conflict issue and Cross-Repetition issue. Thus, we propose a novel approach MMK-BART, which uses a simple but elegant Estimate-Cluster-Penalize mechanism to overcome the mentioned issues and adopts the multilingual language model mBART as the backbone. Meanwhile, based on the recent multilingual corpus XDailyDialog, we propose an MMKRG dataset MMK-DailyDialog, which has been aligned to the large-scale multilingual commonsense knowledge base ConceptNet and supports four languages (English, Chinese, German, and Italian). Extensive experiments have verified the effectiveness of our dataset and approach in monolingual, cross-lingual, and multilingual scenarios.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v38i17.29894
- OA Status
- diamond
- Cited By
- 5
- References
- 32
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393152602Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v38i17.29894Digital Object Identifier
- Title
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Improving Open-Domain Dialogue Response Generation with Multi-Source Multilingual Commonsense KnowledgeWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-24Full publication date if available
- Authors
-
Sixing Wu, Jiong Yu, Jiahao Chen, Xiaofan Deng, Wei ZhouList of authors in order
- Landing page
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https://doi.org/10.1609/aaai.v38i17.29894Publisher landing page
- Open access
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.1609/aaai.v38i17.29894Direct OA link when available
- Concepts
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Domain (mathematical analysis), Computer science, Commonsense knowledge, Open domain, Natural language processing, Artificial intelligence, Domain knowledge, Question answering, Mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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32Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.only | 18 |
| abstract_inverted_index.that | 83 |
| abstract_inverted_index.this | 59 |
| abstract_inverted_index.uses | 74, 108 |
| abstract_inverted_index.(KRG) | 4 |
| abstract_inverted_index.MMKRG | 141 |
| abstract_inverted_index.Prior | 14 |
| abstract_inverted_index.Thus, | 27, 100 |
| abstract_inverted_index.based | 131 |
| abstract_inverted_index.costs | 32 |
| abstract_inverted_index.issue | 96 |
| abstract_inverted_index.mBART | 126 |
| abstract_inverted_index.model | 125 |
| abstract_inverted_index.novel | 104 |
| abstract_inverted_index.scale | 42 |
| abstract_inverted_index.task, | 65 |
| abstract_inverted_index.using | 11 |
| abstract_inverted_index.which | 72, 107, 144 |
| abstract_inverted_index.works | 16 |
| abstract_inverted_index.adopts | 121 |
| abstract_inverted_index.always | 47 |
| abstract_inverted_index.bases, | 39 |
| abstract_inverted_index.corpus | 136 |
| abstract_inverted_index.issue. | 99 |
| abstract_inverted_index.issues | 119 |
| abstract_inverted_index.native | 25 |
| abstract_inverted_index.notice | 82 |
| abstract_inverted_index.recent | 134 |
| abstract_inverted_index.simple | 110 |
| abstract_inverted_index.simply | 84 |
| abstract_inverted_index.German, | 162 |
| abstract_inverted_index.ability | 50 |
| abstract_inverted_index.aligned | 147 |
| abstract_inverted_index.dataset | 142, 173 |
| abstract_inverted_index.elegant | 112 |
| abstract_inverted_index.limited | 41 |
| abstract_inverted_index.propose | 62, 102, 139 |
| abstract_inverted_index.sources | 77 |
| abstract_inverted_index.(MMKRG), | 71 |
| abstract_inverted_index.Chinese, | 161 |
| abstract_inverted_index.Dialogue | 1 |
| abstract_inverted_index.Response | 2, 69 |
| abstract_inverted_index.approach | 105, 175 |
| abstract_inverted_index.external | 12, 37, 45 |
| abstract_inverted_index.fidelity | 9 |
| abstract_inverted_index.language | 56, 124 |
| abstract_inverted_index.multiple | 75 |
| abstract_inverted_index.overcome | 116 |
| abstract_inverted_index.supports | 157 |
| abstract_inverted_index.verified | 168 |
| abstract_inverted_index.(English, | 160 |
| abstract_inverted_index.Extensive | 165 |
| abstract_inverted_index.Italian). | 164 |
| abstract_inverted_index.MMK-BART, | 106 |
| abstract_inverted_index.backbone. | 129 |
| abstract_inverted_index.combining | 85 |
| abstract_inverted_index.dialogues | 10 |
| abstract_inverted_index.different | 79, 88 |
| abstract_inverted_index.knowledge | 21, 38, 46, 76, 86, 153 |
| abstract_inverted_index.language. | 26 |
| abstract_inverted_index.languages | 89, 159 |
| abstract_inverted_index.mechanism | 114 |
| abstract_inverted_index.mentioned | 118 |
| abstract_inverted_index.ConceptNet | 155 |
| abstract_inverted_index.Generation | 3, 70 |
| abstract_inverted_index.Meanwhile, | 130 |
| abstract_inverted_index.accessible | 44 |
| abstract_inverted_index.collecting | 34 |
| abstract_inverted_index.constrains | 48 |
| abstract_inverted_index.especially | 53 |
| abstract_inverted_index.facilitate | 6 |
| abstract_inverted_index.knowledge. | 13 |
| abstract_inverted_index.languages. | 80 |
| abstract_inverted_index.scenarios. | 57, 181 |
| abstract_inverted_index.commonsense | 152 |
| abstract_inverted_index.experiments | 166 |
| abstract_inverted_index.inefficient | 91 |
| abstract_inverted_index.informative | 7 |
| abstract_inverted_index.large-scale | 150 |
| abstract_inverted_index.monolingual | 15 |
| abstract_inverted_index.prohibitive | 31 |
| abstract_inverted_index.Multi-Source | 66 |
| abstract_inverted_index.Multilingual | 67 |
| abstract_inverted_index.constructing | 36 |
| abstract_inverted_index.low-resource | 55 |
| abstract_inverted_index.monolingual, | 177 |
| abstract_inverted_index.multilingual | 123, 135, 151, 180 |
| abstract_inverted_index.XDailyDialog, | 137 |
| abstract_inverted_index.corresponding | 24 |
| abstract_inverted_index.effectiveness | 170 |
| abstract_inverted_index.Cross-Conflict | 95 |
| abstract_inverted_index.cross-lingual, | 178 |
| abstract_inverted_index.simultaneously | 73 |
| abstract_inverted_index.Cross-Repetition | 98 |
| abstract_inverted_index.MMK-DailyDialog, | 143 |
| abstract_inverted_index.Knowledge-Grounded | 68 |
| abstract_inverted_index.Knowledge-grounded | 0 |
| abstract_inverted_index.Estimate-Cluster-Penalize | 113 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.71903005 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |