MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews

Abstract

The advent of the Internet has caused a significant growth in the number of opinions expressed about products or services on e-commerce websites. Aspect category detection, which is one of the challenging subtasks of aspect-based sentiment analysis, deals with categorizing a given review sentence into a set of predefined categories. Most of the research efforts in this field are devoted to English language reviews, while there are a large number of reviews in other languages that are left unexplored. In this paper, we propose a multilingual method to perform aspect category detection on reviews in different languages, which makes use of a deep convolutional neural network with multilingual word embeddings. To the best of our knowledge, our method is the first attempt at performing aspect category detection on multiple languages simultaneously. Empirical results on the multilingual dataset provided by SemEval workshop demonstrate the effectiveness of the proposed method1.

LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification

“…They observed that most languages are effective as cross-lingual augmenters in XNLI and Question Answering tasks. (Ghadery et al, 2019) utilized multilingual word embeddings as word representations and augmented training data by combining training sets in different languages for aspect-based sentiment analysis. Their method is capable of classifying sentences in a specific language when there is no labeled training data available.…”

LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification

“…The authors observed that most languages are effective as cross-lingual augmenters in cross-lingual Natural Language Inference and Question Answering tasks. The MNCN model (Ghadery et al, 2019) utilized multilingual word embeddings as word representations and augmented training data by combining training sets in different languages for aspect-based sentiment analysis. This method is capable of classifying sentences in a specific language when there is no labeled training data available.…”

A Multi-Layer Network for Aspect-Based Cross-Lingual Sentiment Classification

“…Ghadery et al [8] proposed a multi-lingual ngram based CNN for aspect category detection in online reviews. The author used multi-lingual word embedding to deal with multi-lingual data.…”

“…The use of multi-lingual word embedding for the data mapping from one language to another handles cross-lingual data efficiently. However, the performance of multi-lingual word embedding gradually decreases from rich-resource language to poorresource language because of data unavailability [8]. To this end, different neural network-based models are presented to perform multiple NLP tasks [3], [6], [7], [9]- [11], OpenAI [12], ULM-FiT [13], EL-Mo [14], and BERT [15].…”