@inproceedings{han-etal-2022-sancl,
title = "{SANCL}: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning",
author = "Han, Wei and
Chen, Hui and
Hai, Zhen and
Poria, Soujanya and
Bing, Lidong",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.499",
pages = "5666--5677",
abstract = "With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP) that identifies the helpfulness score of multimodal product reviews has become a research hotspot. Previous work on this task focuses on attention-based modality fusion, information integration, and relation modeling, which primarily exposes the following drawbacks: 1) the model may fail to capture the really essential information due to its indiscriminate attention formulation; 2) lack appropriate modeling methods that takes full advantage of correlation among provided data. In this paper, we propose SANCL: Selective Attention and Natural Contrastive Learning for MRHP. SANCL adopts a probe-based strategy to enforce high attention weights on the regions of greater significance. It also constructs a contrastive learning framework based on natural matching properties in the dataset. Experimental results on two benchmark datasets with three categories show that SANCL achieves state-of-the-art baseline performance with lower memory consumption.",
}
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%0 Conference Proceedings
%T SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning
%A Han, Wei
%A Chen, Hui
%A Hai, Zhen
%A Poria, Soujanya
%A Bing, Lidong
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F han-etal-2022-sancl
%X With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP) that identifies the helpfulness score of multimodal product reviews has become a research hotspot. Previous work on this task focuses on attention-based modality fusion, information integration, and relation modeling, which primarily exposes the following drawbacks: 1) the model may fail to capture the really essential information due to its indiscriminate attention formulation; 2) lack appropriate modeling methods that takes full advantage of correlation among provided data. In this paper, we propose SANCL: Selective Attention and Natural Contrastive Learning for MRHP. SANCL adopts a probe-based strategy to enforce high attention weights on the regions of greater significance. It also constructs a contrastive learning framework based on natural matching properties in the dataset. Experimental results on two benchmark datasets with three categories show that SANCL achieves state-of-the-art baseline performance with lower memory consumption.
%U https://aclanthology.org/2022.coling-1.499
%P 5666-5677
Markdown (Informal)
[SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning](https://aclanthology.org/2022.coling-1.499) (Han et al., COLING 2022)
ACL