Video-aided Unsupervised Grammar Induction

Songyang Zhang, Linfeng Song, Lifeng Jin, Kun Xu, Dong Yu, Jiebo Luo


Abstract
We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video. Existing methods of multi-modal grammar induction focus on grammar induction from text-image pairs, with promising results showing that the information from static images is useful in induction. However, videos provide even richer information, including not only static objects but also actions and state changes useful for inducing verb phrases. In this paper, we explore rich features (e.g. action, object, scene, audio, face, OCR and speech) from videos, taking the recent Compound PCFG model as the baseline. We further propose a Multi-Modal Compound PCFG model (MMC-PCFG) to effectively aggregate these rich features from different modalities. Our proposed MMC-PCFG is trained end-to-end and outperforms each individual modality and previous state-of-the-art systems on three benchmarks, i.e. DiDeMo, YouCook2 and MSRVTT, confirming the effectiveness of leveraging video information for unsupervised grammar induction.
Anthology ID:
2021.naacl-main.119
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1513–1524
Language:
URL:
https://aclanthology.org/2021.naacl-main.119
DOI:
10.18653/v1/2021.naacl-main.119
Award:
 Best Long Paper
Bibkey:
Cite (ACL):
Songyang Zhang, Linfeng Song, Lifeng Jin, Kun Xu, Dong Yu, and Jiebo Luo. 2021. Video-aided Unsupervised Grammar Induction. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1513–1524, Online. Association for Computational Linguistics.
Cite (Informal):
Video-aided Unsupervised Grammar Induction (Zhang et al., NAACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.naacl-main.119.pdf
Optional supplementary code:
 2021.naacl-main.119.OptionalSupplementaryCode.zip
Video:
 https://preview.aclanthology.org/ingestion-script-update/2021.naacl-main.119.mp4
Code
 Sy-Zhang/MMC-PCFG
Data
DiDeMoPlacesYouCook2