A Mention-Ranking Model for Abstract Anaphora Resolution

Ana Marasović, Leo Born, Juri Opitz, Anette Frank


Abstract
Resolving abstract anaphora is an important, but difficult task for text understanding. Yet, with recent advances in representation learning this task becomes a more tangible aim. A central property of abstract anaphora is that it establishes a relation between the anaphor embedded in the anaphoric sentence and its (typically non-nominal) antecedent. We propose a mention-ranking model that learns how abstract anaphors relate to their antecedents with an LSTM-Siamese Net. We overcome the lack of training data by generating artificial anaphoric sentence–antecedent pairs. Our model outperforms state-of-the-art results on shell noun resolution. We also report first benchmark results on an abstract anaphora subset of the ARRAU corpus. This corpus presents a greater challenge due to a mixture of nominal and pronominal anaphors and a greater range of confounders. We found model variants that outperform the baselines for nominal anaphors, without training on individual anaphor data, but still lag behind for pronominal anaphors. Our model selects syntactically plausible candidates and – if disregarding syntax – discriminates candidates using deeper features.
Anthology ID:
D17-1021
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
221–232
Language:
URL:
https://aclanthology.org/D17-1021
DOI:
10.18653/v1/D17-1021
Bibkey:
Cite (ACL):
Ana Marasović, Leo Born, Juri Opitz, and Anette Frank. 2017. A Mention-Ranking Model for Abstract Anaphora Resolution. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 221–232, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
A Mention-Ranking Model for Abstract Anaphora Resolution (Marasović et al., EMNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/D17-1021.pdf
Attachment:
 D17-1021.Attachment.pdf
Video:
 https://vimeo.com/238231072
Code
 amarasovic/neural-abstract-anaphora