Collaborative matching for sentence alignment

Xiaojun Quan; Chunyu Kit; Wuya Chen

doi:10.1007/978-3-030-01716-3_4

Collaborative matching for sentence alignment

Xiaojun Quan^*, Chunyu Kit, Wuya Chen

^*Corresponding author for this work

Department of Linguistics and Translation

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

Existing sentence alignment methods are founded fundamentally on sentence length and lexical correspondences. Methods based on the former follow in general the length proportionality assumption that the lengths of sentences in one language tend to be proportional to that of their translations, and are known to bear poor adaptivity to new languages and corpora. In this paper, we attempt to interpret this assumption from a new perspective via the notion of collaborative matching, based on the observation that sentences can work collaboratively during alignment rather than separately as in previous studies. Our approach is tended to be independent on any specific language and corpus, so that it can be adaptively applied to a variety of texts without binding to any prior knowledge about the texts. We use one-to-one sentence alignment to illustrate this approach and implement two specific alignment methods, which are evaluated on six bilingual corpora of different languages and domains. Experimental results confirm the effectiveness of this collaborative matching approach.

Original language	English
Title of host publication	Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data
Subtitle of host publication	17th China National Conference, CCL 2018, and 6th International Symposium, NLP-NABD 2018, Proceedings
Editors	Maosong Sun, Ting Liu, Xiaojie Wang, Zhiyuan Liu, Yang Liu
Publisher	Springer, Cham
Pages	39-52
ISBN (Electronic)	978-3-030-01716-3
ISBN (Print)	978-3-030-01715-6
DOIs	https://doi.org/10.1007/978-3-030-01716-3_4
Publication status	Published - Oct 2018
Event	17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018 - Changsha, China Duration: 19 Oct 2018 → 21 Oct 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11221 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018
Place	China
City	Changsha
Period	19/10/18 → 21/10/18

Research Keywords

Machine translation
Sentence alignment

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10.1007/978-3-030-01716-3_4

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Quan, X., Kit, C., & Chen, W. (2018). Collaborative matching for sentence alignment. In M. Sun, T. Liu, X. Wang, Z. Liu, & Y. Liu (Eds.), Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data : 17th China National Conference, CCL 2018, and 6th International Symposium, NLP-NABD 2018, Proceedings (pp. 39-52). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11221 LNAI). Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_4

Quan, Xiaojun ; Kit, Chunyu ; Chen, Wuya. / Collaborative matching for sentence alignment. Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data : 17th China National Conference, CCL 2018, and 6th International Symposium, NLP-NABD 2018, Proceedings. editor / Maosong Sun ; Ting Liu ; Xiaojie Wang ; Zhiyuan Liu ; Yang Liu. Springer, Cham, 2018. pp. 39-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e2818943aa494f9db973587aea7c57f3,

title = "Collaborative matching for sentence alignment",

abstract = "Existing sentence alignment methods are founded fundamentally on sentence length and lexical correspondences. Methods based on the former follow in general the length proportionality assumption that the lengths of sentences in one language tend to be proportional to that of their translations, and are known to bear poor adaptivity to new languages and corpora. In this paper, we attempt to interpret this assumption from a new perspective via the notion of collaborative matching, based on the observation that sentences can work collaboratively during alignment rather than separately as in previous studies. Our approach is tended to be independent on any specific language and corpus, so that it can be adaptively applied to a variety of texts without binding to any prior knowledge about the texts. We use one-to-one sentence alignment to illustrate this approach and implement two specific alignment methods, which are evaluated on six bilingual corpora of different languages and domains. Experimental results confirm the effectiveness of this collaborative matching approach.",

keywords = "Machine translation, Sentence alignment",

author = "Xiaojun Quan and Chunyu Kit and Wuya Chen",

year = "2018",

month = oct,

doi = "10.1007/978-3-030-01716-3_4",

language = "English",

isbn = "978-3-030-01715-6",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer, Cham",

pages = "39--52",

editor = "Sun, {Maosong } and Ting Liu and Xiaojie Wang and Zhiyuan Liu and Yang Liu",

booktitle = "Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data",

note = "17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018 ; Conference date: 19-10-2018 Through 21-10-2018",

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Quan, X, Kit, C & Chen, W 2018, Collaborative matching for sentence alignment. in M Sun, T Liu, X Wang, Z Liu & Y Liu (eds), Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data : 17th China National Conference, CCL 2018, and 6th International Symposium, NLP-NABD 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11221 LNAI, Springer, Cham, pp. 39-52, 17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018, Changsha, China, 19/10/18. https://doi.org/10.1007/978-3-030-01716-3_4

Collaborative matching for sentence alignment. / Quan, Xiaojun; Kit, Chunyu; Chen, Wuya.
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data : 17th China National Conference, CCL 2018, and 6th International Symposium, NLP-NABD 2018, Proceedings. ed. / Maosong Sun; Ting Liu; Xiaojie Wang; Zhiyuan Liu; Yang Liu. Springer, Cham, 2018. p. 39-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11221 LNAI).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Collaborative matching for sentence alignment

AU - Quan, Xiaojun

AU - Kit, Chunyu

AU - Chen, Wuya

PY - 2018/10

Y1 - 2018/10

N2 - Existing sentence alignment methods are founded fundamentally on sentence length and lexical correspondences. Methods based on the former follow in general the length proportionality assumption that the lengths of sentences in one language tend to be proportional to that of their translations, and are known to bear poor adaptivity to new languages and corpora. In this paper, we attempt to interpret this assumption from a new perspective via the notion of collaborative matching, based on the observation that sentences can work collaboratively during alignment rather than separately as in previous studies. Our approach is tended to be independent on any specific language and corpus, so that it can be adaptively applied to a variety of texts without binding to any prior knowledge about the texts. We use one-to-one sentence alignment to illustrate this approach and implement two specific alignment methods, which are evaluated on six bilingual corpora of different languages and domains. Experimental results confirm the effectiveness of this collaborative matching approach.

AB - Existing sentence alignment methods are founded fundamentally on sentence length and lexical correspondences. Methods based on the former follow in general the length proportionality assumption that the lengths of sentences in one language tend to be proportional to that of their translations, and are known to bear poor adaptivity to new languages and corpora. In this paper, we attempt to interpret this assumption from a new perspective via the notion of collaborative matching, based on the observation that sentences can work collaboratively during alignment rather than separately as in previous studies. Our approach is tended to be independent on any specific language and corpus, so that it can be adaptively applied to a variety of texts without binding to any prior knowledge about the texts. We use one-to-one sentence alignment to illustrate this approach and implement two specific alignment methods, which are evaluated on six bilingual corpora of different languages and domains. Experimental results confirm the effectiveness of this collaborative matching approach.

KW - Machine translation

KW - Sentence alignment

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U2 - 10.1007/978-3-030-01716-3_4

DO - 10.1007/978-3-030-01716-3_4

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-030-01715-6

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 39

EP - 52

BT - Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

A2 - Sun, Maosong

A2 - Liu, Ting

A2 - Wang, Xiaojie

A2 - Liu, Zhiyuan

A2 - Liu, Yang

PB - Springer, Cham

T2 - 17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018

Y2 - 19 October 2018 through 21 October 2018

ER -

Quan X, Kit C, Chen W. Collaborative matching for sentence alignment. In Sun M, Liu T, Wang X, Liu Z, Liu Y, editors, Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data : 17th China National Conference, CCL 2018, and 6th International Symposium, NLP-NABD 2018, Proceedings. Springer, Cham. 2018. p. 39-52. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-01716-3_4

Collaborative matching for sentence alignment

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