Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations Article Swipe
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.15376
With the extensive accumulation of conversational data on the Internet, emotion recognition in conversations (ERC) has received increasing attention. Previous efforts of this task mainly focus on leveraging contextual and speaker-specific features, or integrating heterogeneous external commonsense knowledge. Among them, some heavily rely on future contexts, which, however, are not always available in real-life scenarios. This fact inspires us to generate pseudo future contexts to improve ERC. Specifically, for an utterance, we generate its future context with pre-trained language models, potentially containing extra beneficial knowledge in a conversational form homogeneous with the historical ones. These characteristics make pseudo future contexts easily fused with historical contexts and historical speaker-specific contexts, yielding a conceptually simple framework systematically integrating multi-contexts. Experimental results on four ERC datasets demonstrate our method's superiority. Further in-depth analyses reveal that pseudo future contexts can rival real ones to some extent, especially in relatively context-independent conversations.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.15376
- https://arxiv.org/pdf/2306.15376
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382491573
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4382491573Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2306.15376Digital Object Identifier
- Title
-
Exploiting Pseudo Future Contexts for Emotion Recognition in ConversationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-27Full publication date if available
- Authors
-
Yinyi Wei, Shuaipeng Liu, Hailei Yan, Wei Ye, Tong Mo, Guanglu WanList of authors in order
- Landing page
-
https://arxiv.org/abs/2306.15376Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2306.15376Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2306.15376Direct OA link when available
- Concepts
-
Utterance, Computer science, Focus (optics), Context (archaeology), Task (project management), Homogeneous, Natural language processing, Language understanding, Artificial intelligence, Management, Biology, Paleontology, Thermodynamics, Physics, Optics, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4382491573 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2306.15376 |
| ids.doi | https://doi.org/10.48550/arxiv.2306.15376 |
| ids.openalex | https://openalex.org/W4382491573 |
| fwci | |
| type | preprint |
| title | Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10667 |
| topics[0].field.id | https://openalex.org/fields/32 |
| topics[0].field.display_name | Psychology |
| topics[0].score | 0.989799976348877 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3205 |
| topics[0].subfield.display_name | Experimental and Cognitive Psychology |
| topics[0].display_name | Emotion and Mood Recognition |
| topics[1].id | https://openalex.org/T10664 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9872999787330627 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Sentiment Analysis and Opinion Mining |
| topics[2].id | https://openalex.org/T12031 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9869999885559082 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Speech and dialogue systems |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2775852435 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8102123141288757 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q258403 |
| concepts[0].display_name | Utterance |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.704020619392395 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C192209626 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6604827642440796 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q190909 |
| concepts[2].display_name | Focus (optics) |
| concepts[3].id | https://openalex.org/C2779343474 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6351010799407959 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q3109175 |
| concepts[3].display_name | Context (archaeology) |
| concepts[4].id | https://openalex.org/C2780451532 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5998196005821228 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q759676 |
| concepts[4].display_name | Task (project management) |
| concepts[5].id | https://openalex.org/C66882249 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4664430320262909 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q169336 |
| concepts[5].display_name | Homogeneous |
| concepts[6].id | https://openalex.org/C204321447 |
| concepts[6].level | 1 |
| concepts[6].score | 0.42892542481422424 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[6].display_name | Natural language processing |
| concepts[7].id | https://openalex.org/C2983448237 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4158734083175659 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1078276 |
| concepts[7].display_name | Language understanding |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3497750163078308 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C187736073 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2920921 |
| concepts[9].display_name | Management |
| concepts[10].id | https://openalex.org/C86803240 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[10].display_name | Biology |
| concepts[11].id | https://openalex.org/C151730666 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[11].display_name | Paleontology |
| concepts[12].id | https://openalex.org/C97355855 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11473 |
| concepts[12].display_name | Thermodynamics |
| concepts[13].id | https://openalex.org/C121332964 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[13].display_name | Physics |
| concepts[14].id | https://openalex.org/C120665830 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q14620 |
| concepts[14].display_name | Optics |
| concepts[15].id | https://openalex.org/C162324750 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[15].display_name | Economics |
| keywords[0].id | https://openalex.org/keywords/utterance |
| keywords[0].score | 0.8102123141288757 |
| keywords[0].display_name | Utterance |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.704020619392395 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/focus |
| keywords[2].score | 0.6604827642440796 |
| keywords[2].display_name | Focus (optics) |
| keywords[3].id | https://openalex.org/keywords/context |
| keywords[3].score | 0.6351010799407959 |
| keywords[3].display_name | Context (archaeology) |
| keywords[4].id | https://openalex.org/keywords/task |
| keywords[4].score | 0.5998196005821228 |
| keywords[4].display_name | Task (project management) |
| keywords[5].id | https://openalex.org/keywords/homogeneous |
| keywords[5].score | 0.4664430320262909 |
| keywords[5].display_name | Homogeneous |
| keywords[6].id | https://openalex.org/keywords/natural-language-processing |
| keywords[6].score | 0.42892542481422424 |
| keywords[6].display_name | Natural language processing |
| keywords[7].id | https://openalex.org/keywords/language-understanding |
| keywords[7].score | 0.4158734083175659 |
| keywords[7].display_name | Language understanding |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.3497750163078308 |
| keywords[8].display_name | Artificial intelligence |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2306.15376 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2306.15376 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2306.15376 |
| locations[1].id | doi:10.48550/arxiv.2306.15376 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2306.15376 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5069907865 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4050-3713 |
| authorships[0].author.display_name | Yinyi Wei |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Wei, Yinyi |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5060240389 |
| authorships[1].author.orcid | https://orcid.org/0009-0009-3958-9020 |
| authorships[1].author.display_name | Shuaipeng Liu |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Liu, Shuaipeng |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5104193353 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Hailei Yan |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yan, Hailei |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5101883632 |
| authorships[3].author.orcid | https://orcid.org/0009-0006-0568-9128 |
| authorships[3].author.display_name | Wei Ye |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Ye, Wei |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5059356240 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-3564-4610 |
| authorships[4].author.display_name | Tong Mo |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Mo, Tong |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5072537055 |
| authorships[5].author.orcid | https://orcid.org/0009-0003-1061-3724 |
| authorships[5].author.display_name | Guanglu Wan |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Wan, Guanglu |
| authorships[5].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2306.15376 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-06-29T00:00:00 |
| display_name | Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10667 |
| primary_topic.field.id | https://openalex.org/fields/32 |
| primary_topic.field.display_name | Psychology |
| primary_topic.score | 0.989799976348877 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3205 |
| primary_topic.subfield.display_name | Experimental and Cognitive Psychology |
| primary_topic.display_name | Emotion and Mood Recognition |
| related_works | https://openalex.org/W2529301793, https://openalex.org/W2384121599, https://openalex.org/W2038083449, https://openalex.org/W3177678247, https://openalex.org/W2333799855, https://openalex.org/W2947571721, https://openalex.org/W3100661441, https://openalex.org/W1995281866, https://openalex.org/W4287601611, https://openalex.org/W3161060657 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2306.15376 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2306.15376 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2306.15376 |
| primary_location.id | pmh:oai:arXiv.org:2306.15376 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2306.15376 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2306.15376 |
| publication_date | 2023-06-27 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 86, 110 |
| abstract_inverted_index.an | 69 |
| abstract_inverted_index.in | 12, 52, 85, 143 |
| abstract_inverted_index.of | 4, 21 |
| abstract_inverted_index.on | 7, 26, 43, 119 |
| abstract_inverted_index.or | 32 |
| abstract_inverted_index.to | 59, 64, 139 |
| abstract_inverted_index.us | 58 |
| abstract_inverted_index.we | 71 |
| abstract_inverted_index.ERC | 121 |
| abstract_inverted_index.and | 29, 105 |
| abstract_inverted_index.are | 48 |
| abstract_inverted_index.can | 135 |
| abstract_inverted_index.for | 68 |
| abstract_inverted_index.has | 15 |
| abstract_inverted_index.its | 73 |
| abstract_inverted_index.not | 49 |
| abstract_inverted_index.our | 124 |
| abstract_inverted_index.the | 1, 8, 91 |
| abstract_inverted_index.ERC. | 66 |
| abstract_inverted_index.This | 55 |
| abstract_inverted_index.With | 0 |
| abstract_inverted_index.data | 6 |
| abstract_inverted_index.fact | 56 |
| abstract_inverted_index.form | 88 |
| abstract_inverted_index.four | 120 |
| abstract_inverted_index.make | 96 |
| abstract_inverted_index.ones | 138 |
| abstract_inverted_index.real | 137 |
| abstract_inverted_index.rely | 42 |
| abstract_inverted_index.some | 40, 140 |
| abstract_inverted_index.task | 23 |
| abstract_inverted_index.that | 131 |
| abstract_inverted_index.this | 22 |
| abstract_inverted_index.with | 76, 90, 102 |
| abstract_inverted_index.(ERC) | 14 |
| abstract_inverted_index.Among | 38 |
| abstract_inverted_index.These | 94 |
| abstract_inverted_index.extra | 82 |
| abstract_inverted_index.focus | 25 |
| abstract_inverted_index.fused | 101 |
| abstract_inverted_index.ones. | 93 |
| abstract_inverted_index.rival | 136 |
| abstract_inverted_index.them, | 39 |
| abstract_inverted_index.always | 50 |
| abstract_inverted_index.easily | 100 |
| abstract_inverted_index.future | 44, 62, 74, 98, 133 |
| abstract_inverted_index.mainly | 24 |
| abstract_inverted_index.pseudo | 61, 97, 132 |
| abstract_inverted_index.reveal | 130 |
| abstract_inverted_index.simple | 112 |
| abstract_inverted_index.which, | 46 |
| abstract_inverted_index.Further | 127 |
| abstract_inverted_index.context | 75 |
| abstract_inverted_index.efforts | 20 |
| abstract_inverted_index.emotion | 10 |
| abstract_inverted_index.extent, | 141 |
| abstract_inverted_index.heavily | 41 |
| abstract_inverted_index.improve | 65 |
| abstract_inverted_index.models, | 79 |
| abstract_inverted_index.results | 118 |
| abstract_inverted_index.Previous | 19 |
| abstract_inverted_index.analyses | 129 |
| abstract_inverted_index.contexts | 63, 99, 104, 134 |
| abstract_inverted_index.datasets | 122 |
| abstract_inverted_index.external | 35 |
| abstract_inverted_index.generate | 60, 72 |
| abstract_inverted_index.however, | 47 |
| abstract_inverted_index.in-depth | 128 |
| abstract_inverted_index.inspires | 57 |
| abstract_inverted_index.language | 78 |
| abstract_inverted_index.method's | 125 |
| abstract_inverted_index.received | 16 |
| abstract_inverted_index.yielding | 109 |
| abstract_inverted_index.Internet, | 9 |
| abstract_inverted_index.available | 51 |
| abstract_inverted_index.contexts, | 45, 108 |
| abstract_inverted_index.extensive | 2 |
| abstract_inverted_index.features, | 31 |
| abstract_inverted_index.framework | 113 |
| abstract_inverted_index.knowledge | 84 |
| abstract_inverted_index.real-life | 53 |
| abstract_inverted_index.attention. | 18 |
| abstract_inverted_index.beneficial | 83 |
| abstract_inverted_index.containing | 81 |
| abstract_inverted_index.contextual | 28 |
| abstract_inverted_index.especially | 142 |
| abstract_inverted_index.historical | 92, 103, 106 |
| abstract_inverted_index.increasing | 17 |
| abstract_inverted_index.knowledge. | 37 |
| abstract_inverted_index.leveraging | 27 |
| abstract_inverted_index.relatively | 144 |
| abstract_inverted_index.scenarios. | 54 |
| abstract_inverted_index.utterance, | 70 |
| abstract_inverted_index.commonsense | 36 |
| abstract_inverted_index.demonstrate | 123 |
| abstract_inverted_index.homogeneous | 89 |
| abstract_inverted_index.integrating | 33, 115 |
| abstract_inverted_index.potentially | 80 |
| abstract_inverted_index.pre-trained | 77 |
| abstract_inverted_index.recognition | 11 |
| abstract_inverted_index.Experimental | 117 |
| abstract_inverted_index.accumulation | 3 |
| abstract_inverted_index.conceptually | 111 |
| abstract_inverted_index.superiority. | 126 |
| abstract_inverted_index.Specifically, | 67 |
| abstract_inverted_index.conversations | 13 |
| abstract_inverted_index.heterogeneous | 34 |
| abstract_inverted_index.conversational | 5, 87 |
| abstract_inverted_index.conversations. | 146 |
| abstract_inverted_index.systematically | 114 |
| abstract_inverted_index.characteristics | 95 |
| abstract_inverted_index.multi-contexts. | 116 |
| abstract_inverted_index.speaker-specific | 30, 107 |
| abstract_inverted_index.context-independent | 145 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |