Review of Conversational Machine Reading Comprehension
Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn que...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
LI Kun, LI Yanling, LIN Min [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Chinesisch |
| Erschienen: |
2021 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
In: Jisuanji kexue yu tansuo - Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press, 2021, 15(2021), 9, Seite 1607-1618 |
|---|---|
| Übergeordnetes Werk: |
volume:15 ; year:2021 ; number:9 ; pages:1607-1618 |
| Links: |
|---|
| DOI / URN: |
10.3778/j.issn.1673-9418.2102039 |
|---|
| Katalog-ID: |
DOAJ003886409 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | DOAJ003886409 | ||
| 003 | DE-627 | ||
| 005 | 20230309180915.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230225s2021 xx |||||o 00| ||chi c | ||
| 024 | 7 | |a 10.3778/j.issn.1673-9418.2102039 |2 doi | |
| 035 | |a (DE-627)DOAJ003886409 | ||
| 035 | |a (DE-599)DOAJ511ceaec5aa84b2a926d30cabcfaddf5 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a chi | ||
| 050 | 0 | |a QA75.5-76.95 | |
| 100 | 0 | |a LI Kun, LI Yanling, LIN Min |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Review of Conversational Machine Reading Comprehension |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn question answering, and there is no dependency between question answering pairs. Conversational question answering (ConvQA) is the human-computer process that enables the machine to carry out continuous topics when it helps human to obtain information. In recent years, with the development of MRC datasets and deep neural networks, researchers combine machine reading com-prehension and conversational question answering to form a new more complex field, conversational machine com-prehension (CMC). This has greatly boosted the field of MRC. This paper summarizes the latest research progress of CMC in recent years from three aspects. Firstly, it describes the definition of CMC task, the challenges and the characteristics of the datasets. Then, it summarizes the research progress and the current architecture of the latest models, and focuses on the relevant technical methods used in conversational history embedding and conversational reasoning. Finally, this paper analyzes the current model of CMC, and prospects the future research hotspots and methods of CMC. | ||
| 650 | 4 | |a multi-turn conversation | |
| 650 | 4 | |a machine reading comprehension (mrc) | |
| 650 | 4 | |a conversational question answering (convqa) | |
| 653 | 0 | |a Electronic computers. Computer science | |
| 773 | 0 | 8 | |i In |t Jisuanji kexue yu tansuo |d Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press, 2021 |g 15(2021), 9, Seite 1607-1618 |w (DE-627)DOAJ078619211 |x 16739418 |7 nnns |
| 773 | 1 | 8 | |g volume:15 |g year:2021 |g number:9 |g pages:1607-1618 |
| 856 | 4 | 0 | |u https://doi.org/10.3778/j.issn.1673-9418.2102039 |z kostenfrei |
| 856 | 4 | 0 | |u https://doaj.org/article/511ceaec5aa84b2a926d30cabcfaddf5 |z kostenfrei |
| 856 | 4 | 0 | |u http://fcst.ceaj.org/CN/abstract/abstract2875.shtml |z kostenfrei |
| 856 | 4 | 2 | |u https://doaj.org/toc/1673-9418 |y Journal toc |z kostenfrei |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_DOAJ | ||
| 951 | |a AR | ||
| 952 | |d 15 |j 2021 |e 9 |h 1607-1618 | ||
| author_variant |
k l y l m l klylm klylml |
|---|---|
| matchkey_str |
article:16739418:2021----::eiwfovrainlahnraig |
| hierarchy_sort_str |
2021 |
| callnumber-subject-code |
QA |
| publishDate |
2021 |
| allfields |
10.3778/j.issn.1673-9418.2102039 doi (DE-627)DOAJ003886409 (DE-599)DOAJ511ceaec5aa84b2a926d30cabcfaddf5 DE-627 ger DE-627 rakwb chi QA75.5-76.95 LI Kun, LI Yanling, LIN Min verfasserin aut Review of Conversational Machine Reading Comprehension 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn question answering, and there is no dependency between question answering pairs. Conversational question answering (ConvQA) is the human-computer process that enables the machine to carry out continuous topics when it helps human to obtain information. In recent years, with the development of MRC datasets and deep neural networks, researchers combine machine reading com-prehension and conversational question answering to form a new more complex field, conversational machine com-prehension (CMC). This has greatly boosted the field of MRC. This paper summarizes the latest research progress of CMC in recent years from three aspects. Firstly, it describes the definition of CMC task, the challenges and the characteristics of the datasets. Then, it summarizes the research progress and the current architecture of the latest models, and focuses on the relevant technical methods used in conversational history embedding and conversational reasoning. Finally, this paper analyzes the current model of CMC, and prospects the future research hotspots and methods of CMC. multi-turn conversation machine reading comprehension (mrc) conversational question answering (convqa) Electronic computers. Computer science In Jisuanji kexue yu tansuo Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press, 2021 15(2021), 9, Seite 1607-1618 (DE-627)DOAJ078619211 16739418 nnns volume:15 year:2021 number:9 pages:1607-1618 https://doi.org/10.3778/j.issn.1673-9418.2102039 kostenfrei https://doaj.org/article/511ceaec5aa84b2a926d30cabcfaddf5 kostenfrei http://fcst.ceaj.org/CN/abstract/abstract2875.shtml kostenfrei https://doaj.org/toc/1673-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 15 2021 9 1607-1618 |
| spelling |
10.3778/j.issn.1673-9418.2102039 doi (DE-627)DOAJ003886409 (DE-599)DOAJ511ceaec5aa84b2a926d30cabcfaddf5 DE-627 ger DE-627 rakwb chi QA75.5-76.95 LI Kun, LI Yanling, LIN Min verfasserin aut Review of Conversational Machine Reading Comprehension 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn question answering, and there is no dependency between question answering pairs. Conversational question answering (ConvQA) is the human-computer process that enables the machine to carry out continuous topics when it helps human to obtain information. In recent years, with the development of MRC datasets and deep neural networks, researchers combine machine reading com-prehension and conversational question answering to form a new more complex field, conversational machine com-prehension (CMC). This has greatly boosted the field of MRC. This paper summarizes the latest research progress of CMC in recent years from three aspects. Firstly, it describes the definition of CMC task, the challenges and the characteristics of the datasets. Then, it summarizes the research progress and the current architecture of the latest models, and focuses on the relevant technical methods used in conversational history embedding and conversational reasoning. Finally, this paper analyzes the current model of CMC, and prospects the future research hotspots and methods of CMC. multi-turn conversation machine reading comprehension (mrc) conversational question answering (convqa) Electronic computers. Computer science In Jisuanji kexue yu tansuo Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press, 2021 15(2021), 9, Seite 1607-1618 (DE-627)DOAJ078619211 16739418 nnns volume:15 year:2021 number:9 pages:1607-1618 https://doi.org/10.3778/j.issn.1673-9418.2102039 kostenfrei https://doaj.org/article/511ceaec5aa84b2a926d30cabcfaddf5 kostenfrei http://fcst.ceaj.org/CN/abstract/abstract2875.shtml kostenfrei https://doaj.org/toc/1673-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 15 2021 9 1607-1618 |
| allfields_unstemmed |
10.3778/j.issn.1673-9418.2102039 doi (DE-627)DOAJ003886409 (DE-599)DOAJ511ceaec5aa84b2a926d30cabcfaddf5 DE-627 ger DE-627 rakwb chi QA75.5-76.95 LI Kun, LI Yanling, LIN Min verfasserin aut Review of Conversational Machine Reading Comprehension 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn question answering, and there is no dependency between question answering pairs. Conversational question answering (ConvQA) is the human-computer process that enables the machine to carry out continuous topics when it helps human to obtain information. In recent years, with the development of MRC datasets and deep neural networks, researchers combine machine reading com-prehension and conversational question answering to form a new more complex field, conversational machine com-prehension (CMC). This has greatly boosted the field of MRC. This paper summarizes the latest research progress of CMC in recent years from three aspects. Firstly, it describes the definition of CMC task, the challenges and the characteristics of the datasets. Then, it summarizes the research progress and the current architecture of the latest models, and focuses on the relevant technical methods used in conversational history embedding and conversational reasoning. Finally, this paper analyzes the current model of CMC, and prospects the future research hotspots and methods of CMC. multi-turn conversation machine reading comprehension (mrc) conversational question answering (convqa) Electronic computers. Computer science In Jisuanji kexue yu tansuo Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press, 2021 15(2021), 9, Seite 1607-1618 (DE-627)DOAJ078619211 16739418 nnns volume:15 year:2021 number:9 pages:1607-1618 https://doi.org/10.3778/j.issn.1673-9418.2102039 kostenfrei https://doaj.org/article/511ceaec5aa84b2a926d30cabcfaddf5 kostenfrei http://fcst.ceaj.org/CN/abstract/abstract2875.shtml kostenfrei https://doaj.org/toc/1673-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 15 2021 9 1607-1618 |
| allfieldsGer |
10.3778/j.issn.1673-9418.2102039 doi (DE-627)DOAJ003886409 (DE-599)DOAJ511ceaec5aa84b2a926d30cabcfaddf5 DE-627 ger DE-627 rakwb chi QA75.5-76.95 LI Kun, LI Yanling, LIN Min verfasserin aut Review of Conversational Machine Reading Comprehension 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn question answering, and there is no dependency between question answering pairs. Conversational question answering (ConvQA) is the human-computer process that enables the machine to carry out continuous topics when it helps human to obtain information. In recent years, with the development of MRC datasets and deep neural networks, researchers combine machine reading com-prehension and conversational question answering to form a new more complex field, conversational machine com-prehension (CMC). This has greatly boosted the field of MRC. This paper summarizes the latest research progress of CMC in recent years from three aspects. Firstly, it describes the definition of CMC task, the challenges and the characteristics of the datasets. Then, it summarizes the research progress and the current architecture of the latest models, and focuses on the relevant technical methods used in conversational history embedding and conversational reasoning. Finally, this paper analyzes the current model of CMC, and prospects the future research hotspots and methods of CMC. multi-turn conversation machine reading comprehension (mrc) conversational question answering (convqa) Electronic computers. Computer science In Jisuanji kexue yu tansuo Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press, 2021 15(2021), 9, Seite 1607-1618 (DE-627)DOAJ078619211 16739418 nnns volume:15 year:2021 number:9 pages:1607-1618 https://doi.org/10.3778/j.issn.1673-9418.2102039 kostenfrei https://doaj.org/article/511ceaec5aa84b2a926d30cabcfaddf5 kostenfrei http://fcst.ceaj.org/CN/abstract/abstract2875.shtml kostenfrei https://doaj.org/toc/1673-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 15 2021 9 1607-1618 |
| allfieldsSound |
10.3778/j.issn.1673-9418.2102039 doi (DE-627)DOAJ003886409 (DE-599)DOAJ511ceaec5aa84b2a926d30cabcfaddf5 DE-627 ger DE-627 rakwb chi QA75.5-76.95 LI Kun, LI Yanling, LIN Min verfasserin aut Review of Conversational Machine Reading Comprehension 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn question answering, and there is no dependency between question answering pairs. Conversational question answering (ConvQA) is the human-computer process that enables the machine to carry out continuous topics when it helps human to obtain information. In recent years, with the development of MRC datasets and deep neural networks, researchers combine machine reading com-prehension and conversational question answering to form a new more complex field, conversational machine com-prehension (CMC). This has greatly boosted the field of MRC. This paper summarizes the latest research progress of CMC in recent years from three aspects. Firstly, it describes the definition of CMC task, the challenges and the characteristics of the datasets. Then, it summarizes the research progress and the current architecture of the latest models, and focuses on the relevant technical methods used in conversational history embedding and conversational reasoning. Finally, this paper analyzes the current model of CMC, and prospects the future research hotspots and methods of CMC. multi-turn conversation machine reading comprehension (mrc) conversational question answering (convqa) Electronic computers. Computer science In Jisuanji kexue yu tansuo Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press, 2021 15(2021), 9, Seite 1607-1618 (DE-627)DOAJ078619211 16739418 nnns volume:15 year:2021 number:9 pages:1607-1618 https://doi.org/10.3778/j.issn.1673-9418.2102039 kostenfrei https://doaj.org/article/511ceaec5aa84b2a926d30cabcfaddf5 kostenfrei http://fcst.ceaj.org/CN/abstract/abstract2875.shtml kostenfrei https://doaj.org/toc/1673-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 15 2021 9 1607-1618 |
| language |
Chinese |
| source |
In Jisuanji kexue yu tansuo 15(2021), 9, Seite 1607-1618 volume:15 year:2021 number:9 pages:1607-1618 |
| sourceStr |
In Jisuanji kexue yu tansuo 15(2021), 9, Seite 1607-1618 volume:15 year:2021 number:9 pages:1607-1618 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
multi-turn conversation machine reading comprehension (mrc) conversational question answering (convqa) Electronic computers. Computer science |
| isfreeaccess_bool |
true |
| container_title |
Jisuanji kexue yu tansuo |
| authorswithroles_txt_mv |
LI Kun, LI Yanling, LIN Min @@aut@@ |
| publishDateDaySort_date |
2021-01-01T00:00:00Z |
| hierarchy_top_id |
DOAJ078619211 |
| id |
DOAJ003886409 |
| language_de |
chinesisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ003886409</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309180915.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2021 xx |||||o 00| ||chi c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3778/j.issn.1673-9418.2102039</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ003886409</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ511ceaec5aa84b2a926d30cabcfaddf5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">chi</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA75.5-76.95</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">LI Kun, LI Yanling, LIN Min</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Review of Conversational Machine Reading Comprehension</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn question answering, and there is no dependency between question answering pairs. Conversational question answering (ConvQA) is the human-computer process that enables the machine to carry out continuous topics when it helps human to obtain information. In recent years, with the development of MRC datasets and deep neural networks, researchers combine machine reading com-prehension and conversational question answering to form a new more complex field, conversational machine com-prehension (CMC). This has greatly boosted the field of MRC. This paper summarizes the latest research progress of CMC in recent years from three aspects. Firstly, it describes the definition of CMC task, the challenges and the characteristics of the datasets. Then, it summarizes the research progress and the current architecture of the latest models, and focuses on the relevant technical methods used in conversational history embedding and conversational reasoning. Finally, this paper analyzes the current model of CMC, and prospects the future research hotspots and methods of CMC.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-turn conversation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine reading comprehension (mrc)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">conversational question answering (convqa)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronic computers. Computer science</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Jisuanji kexue yu tansuo</subfield><subfield code="d">Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press, 2021</subfield><subfield code="g">15(2021), 9, Seite 1607-1618</subfield><subfield code="w">(DE-627)DOAJ078619211</subfield><subfield code="x">16739418</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:9</subfield><subfield code="g">pages:1607-1618</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3778/j.issn.1673-9418.2102039</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/511ceaec5aa84b2a926d30cabcfaddf5</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://fcst.ceaj.org/CN/abstract/abstract2875.shtml</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1673-9418</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2021</subfield><subfield code="e">9</subfield><subfield code="h">1607-1618</subfield></datafield></record></collection>
|
| callnumber-first |
Q - Science |
| author |
LI Kun, LI Yanling, LIN Min |
| spellingShingle |
LI Kun, LI Yanling, LIN Min misc QA75.5-76.95 misc multi-turn conversation misc machine reading comprehension (mrc) misc conversational question answering (convqa) misc Electronic computers. Computer science Review of Conversational Machine Reading Comprehension |
| authorStr |
LI Kun, LI Yanling, LIN Min |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)DOAJ078619211 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
DOAJ |
| remote_str |
true |
| callnumber-label |
QA75 |
| illustrated |
Not Illustrated |
| issn |
16739418 |
| topic_title |
QA75.5-76.95 Review of Conversational Machine Reading Comprehension multi-turn conversation machine reading comprehension (mrc) conversational question answering (convqa) |
| topic |
misc QA75.5-76.95 misc multi-turn conversation misc machine reading comprehension (mrc) misc conversational question answering (convqa) misc Electronic computers. Computer science |
| topic_unstemmed |
misc QA75.5-76.95 misc multi-turn conversation misc machine reading comprehension (mrc) misc conversational question answering (convqa) misc Electronic computers. Computer science |
| topic_browse |
misc QA75.5-76.95 misc multi-turn conversation misc machine reading comprehension (mrc) misc conversational question answering (convqa) misc Electronic computers. Computer science |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Jisuanji kexue yu tansuo |
| hierarchy_parent_id |
DOAJ078619211 |
| hierarchy_top_title |
Jisuanji kexue yu tansuo |
| isfreeaccess_txt |
true |
| familylinks_str_mv |
(DE-627)DOAJ078619211 |
| title |
Review of Conversational Machine Reading Comprehension |
| ctrlnum |
(DE-627)DOAJ003886409 (DE-599)DOAJ511ceaec5aa84b2a926d30cabcfaddf5 |
| title_full |
Review of Conversational Machine Reading Comprehension |
| author_sort |
LI Kun, LI Yanling, LIN Min |
| journal |
Jisuanji kexue yu tansuo |
| journalStr |
Jisuanji kexue yu tansuo |
| callnumber-first-code |
Q |
| lang_code |
chi |
| isOA_bool |
true |
| recordtype |
marc |
| publishDateSort |
2021 |
| contenttype_str_mv |
txt |
| container_start_page |
1607 |
| author_browse |
LI Kun, LI Yanling, LIN Min |
| container_volume |
15 |
| class |
QA75.5-76.95 |
| format_se |
Elektronische Aufsätze |
| author-letter |
LI Kun, LI Yanling, LIN Min |
| doi_str_mv |
10.3778/j.issn.1673-9418.2102039 |
| title_sort |
review of conversational machine reading comprehension |
| callnumber |
QA75.5-76.95 |
| title_auth |
Review of Conversational Machine Reading Comprehension |
| abstract |
Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn question answering, and there is no dependency between question answering pairs. Conversational question answering (ConvQA) is the human-computer process that enables the machine to carry out continuous topics when it helps human to obtain information. In recent years, with the development of MRC datasets and deep neural networks, researchers combine machine reading com-prehension and conversational question answering to form a new more complex field, conversational machine com-prehension (CMC). This has greatly boosted the field of MRC. This paper summarizes the latest research progress of CMC in recent years from three aspects. Firstly, it describes the definition of CMC task, the challenges and the characteristics of the datasets. Then, it summarizes the research progress and the current architecture of the latest models, and focuses on the relevant technical methods used in conversational history embedding and conversational reasoning. Finally, this paper analyzes the current model of CMC, and prospects the future research hotspots and methods of CMC. |
| abstractGer |
Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn question answering, and there is no dependency between question answering pairs. Conversational question answering (ConvQA) is the human-computer process that enables the machine to carry out continuous topics when it helps human to obtain information. In recent years, with the development of MRC datasets and deep neural networks, researchers combine machine reading com-prehension and conversational question answering to form a new more complex field, conversational machine com-prehension (CMC). This has greatly boosted the field of MRC. This paper summarizes the latest research progress of CMC in recent years from three aspects. Firstly, it describes the definition of CMC task, the challenges and the characteristics of the datasets. Then, it summarizes the research progress and the current architecture of the latest models, and focuses on the relevant technical methods used in conversational history embedding and conversational reasoning. Finally, this paper analyzes the current model of CMC, and prospects the future research hotspots and methods of CMC. |
| abstract_unstemmed |
Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn question answering, and there is no dependency between question answering pairs. Conversational question answering (ConvQA) is the human-computer process that enables the machine to carry out continuous topics when it helps human to obtain information. In recent years, with the development of MRC datasets and deep neural networks, researchers combine machine reading com-prehension and conversational question answering to form a new more complex field, conversational machine com-prehension (CMC). This has greatly boosted the field of MRC. This paper summarizes the latest research progress of CMC in recent years from three aspects. Firstly, it describes the definition of CMC task, the challenges and the characteristics of the datasets. Then, it summarizes the research progress and the current architecture of the latest models, and focuses on the relevant technical methods used in conversational history embedding and conversational reasoning. Finally, this paper analyzes the current model of CMC, and prospects the future research hotspots and methods of CMC. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ |
| container_issue |
9 |
| title_short |
Review of Conversational Machine Reading Comprehension |
| url |
https://doi.org/10.3778/j.issn.1673-9418.2102039 https://doaj.org/article/511ceaec5aa84b2a926d30cabcfaddf5 http://fcst.ceaj.org/CN/abstract/abstract2875.shtml https://doaj.org/toc/1673-9418 |
| remote_bool |
true |
| ppnlink |
DOAJ078619211 |
| callnumber-subject |
QA - Mathematics |
| mediatype_str_mv |
c |
| isOA_txt |
true |
| hochschulschrift_bool |
false |
| doi_str |
10.3778/j.issn.1673-9418.2102039 |
| callnumber-a |
QA75.5-76.95 |
| up_date |
2024-07-03T20:41:35.158Z |
| _version_ |
1803591923964837888 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ003886409</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309180915.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2021 xx |||||o 00| ||chi c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3778/j.issn.1673-9418.2102039</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ003886409</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ511ceaec5aa84b2a926d30cabcfaddf5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">chi</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA75.5-76.95</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">LI Kun, LI Yanling, LIN Min</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Review of Conversational Machine Reading Comprehension</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text. But limited by datasets, most of the machine reading comprehension tasks are single-turn question answering, and there is no dependency between question answering pairs. Conversational question answering (ConvQA) is the human-computer process that enables the machine to carry out continuous topics when it helps human to obtain information. In recent years, with the development of MRC datasets and deep neural networks, researchers combine machine reading com-prehension and conversational question answering to form a new more complex field, conversational machine com-prehension (CMC). This has greatly boosted the field of MRC. This paper summarizes the latest research progress of CMC in recent years from three aspects. Firstly, it describes the definition of CMC task, the challenges and the characteristics of the datasets. Then, it summarizes the research progress and the current architecture of the latest models, and focuses on the relevant technical methods used in conversational history embedding and conversational reasoning. Finally, this paper analyzes the current model of CMC, and prospects the future research hotspots and methods of CMC.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-turn conversation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine reading comprehension (mrc)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">conversational question answering (convqa)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronic computers. Computer science</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Jisuanji kexue yu tansuo</subfield><subfield code="d">Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press, 2021</subfield><subfield code="g">15(2021), 9, Seite 1607-1618</subfield><subfield code="w">(DE-627)DOAJ078619211</subfield><subfield code="x">16739418</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:9</subfield><subfield code="g">pages:1607-1618</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3778/j.issn.1673-9418.2102039</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/511ceaec5aa84b2a926d30cabcfaddf5</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://fcst.ceaj.org/CN/abstract/abstract2875.shtml</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1673-9418</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2021</subfield><subfield code="e">9</subfield><subfield code="h">1607-1618</subfield></datafield></record></collection>
|
| score |
7.3973074 |