Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning

By Xinjie Zhou, Xiaojun Wan, Jianguo Xiao. 2016

118 citations recorded by the source corpus.

Cross-lingual sentiment classiﬁcation aims to adapt the sentiment resource in a resource-rich language to a resource-poor language. In this study, we propose a representation learning approach which simultaneously learns vector representations for the texts in both the source and the target languages. Different from previous research which only gets bilingual word embedding, our Bilingual Document Representation Learning model BiDRL directly learns document representations. Both semantic and sentiment correlations are utilized to map the bilingual texts into the same embedding space. The experiments are based on the multilingual multi-domain Amazon review dataset. We use English as the source language and use Japanese, German and French as the target languages. The experimental results show that BiDRL outperforms the state-of-the-art methods for all the target languages.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

XNLI: Evaluating Cross-lingual Sentence Representations (2018, background): “Zhou et al. (2016) learn bilingual document representations by minimizing the Euclidean distance between document representations and their translations.”
Filtering and Mining Parallel Data in a Joint Multilingual Space (2018, background): “There are several works on learning multilingual sentence embeddings which could be used for that purpose, i.e. (Hermann and Blunsom, 2014; Pham et al., 2015; Zhou et al., 2016; Chan-dar et al., 2013; Mogadala and Rettinger, 2016).”
Learning Joint Multilingual Sentence Representations with Neural Machine Translation (2017, background): “There are several works on learning multilingual representations at document level (Hermann and Blunsom, 2014; Zhou et al., 2016b; Pham et al., 2015).”
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond (2021, unknown): “…Sentiment Analysis Benchmark , UMSAB henceforth), as this task is by far the most studied problem in NLP in Twitter (cf., e.g., (Salameh et al., 2015; Zhou et al., 2016; Meng et al., 2012; Chen et al., 2018; Rasooli et al., 2018; Vilares et al., 2017; Barnes et al., 2019; Patwa et al., 2020;…”
Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations (2018, methodology): “Several authors proposed to extend ParagraphVec (Le and Mikolov, 2014) to the cross-lingual case: Pham et al. (2015) add a bilingual constraint to learn cross-lingual representations using aligned sentences; Mogadala and Rettinger (2016) add a general cross-lingual regularization term to Para-graphV”
Ranking Transfer Languages with Pragmatically-Motivated Features for Multilingual Sentiment Analysis (2020, background): “…natural language processing tasks, including machine translation (Zoph et al., 2016; Johnson et al., 2017; Neubig and Hu, 2018), multilingual dependency parsing (Ammar et al., 2016; Ponti et al., 2018), and multilingual sentiment analysis (Prettenhofer and Stein, 2010; Zhou et al., 2016).”
XCMRC: Evaluating Cross-lingual Machine Reading Comprehension (2019, background): “Zhou (2016) learned bilingual document representations by minimizing the Euclidean distance between document representations and their translations [30].”