Enhancing sentiment and emotion translation of review text through MLM knowledge integration in NMT

IF 2.3 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Divya Kumari, Asif Ekbal
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Abstract

Producing a high-quality review translation is a multifaceted process. It goes beyond successful semantic transfer and requires conveying the original message’s tone and style in a way that resonates with the target audience, whether they are human readers or Natural Language Processing (NLP) applications. Capturing these subtle nuances of the review text demands a deeper understanding and better encoding of the source message. In order to achieve this goal, we explore the use of self-supervised masked language modeling (MLM) and a variant called polarity masked language modeling (p-MLM) as auxiliary tasks in a multi-learning setup. MLM is widely recognized for its ability to capture rich linguistic representations of the input and has been shown to achieve state-of-the-art accuracy in various language understanding tasks. Motivated by its effectiveness, in this paper we adopt joint learning, combining the neural machine translation (NMT) task with source polarity-masked language modeling within a shared embedding space to induce a deeper understanding of the emotional nuances of the text. We analyze the results and observe that our multi-task model indeed exhibits a better understanding of linguistic concepts like sentiment and emotion. Intriguingly, this is achieved even without explicit training on sentiment-annotated or domain-specific sentiment corpora. Our multi-task NMT model consistently improves the translation quality of affect sentences from diverse domains in three language pairs.

Abstract Image

通过在 NMT 中整合 MLM 知识,加强评论文本的情感和情绪翻译
高质量的评论翻译是一个多方面的过程。它不仅仅是成功的语义转换,还要求以一种能与目标受众(无论是人类读者还是自然语言处理 (NLP) 应用程序)产生共鸣的方式传达原始信息的语气和风格。要捕捉评论文本中这些细微的差别,就需要对源信息有更深入的理解和更好的编码。为了实现这一目标,我们探索了在多重学习设置中使用自监督掩蔽语言建模(MLM)和称为极性掩蔽语言建模(p-MLM)的变体作为辅助任务。MLM 因其捕捉输入的丰富语言表征的能力而得到广泛认可,并已被证明在各种语言理解任务中达到了最先进的准确度。受其有效性的激励,我们在本文中采用了联合学习的方法,将神经机器翻译(NMT)任务与共享嵌入空间中的源极性掩蔽语言建模相结合,以加深对文本情感细微差别的理解。我们对结果进行了分析,发现我们的多任务模型确实能更好地理解情感和情绪等语言概念。耐人寻味的是,即使没有对情感注释或特定领域的情感语料库进行明确的训练,我们也能做到这一点。我们的多任务 NMT 模型在三种语言对中持续提高了来自不同领域的情感句子的翻译质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems 工程技术-计算机:人工智能
CiteScore
7.20
自引率
11.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.
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