Aspect-based sentiment analysis involves the recognition of so called opinion target expressions ( OTEs ) . To automatically extract OTEs , supervised learning algorithms are usually employed which are trained on manually annotated corpora . The creation of these corpora is labor-intensive and sufficiently large datasets are therefore usually only available for a very narrow selection of languages and domains . In this work , we address the lack of available annotated data for specific languages by proposing a zero-shot cross-lingual approach for the extraction of opinion target expressions . We leverage multilingual word embeddings that share a common vector space across various languages and incorporate these into a convolutional neural network architecture for OTE extraction . Our experiments with 5 languages give promising results : We can successfully train a model on annotated data of a source language and perform accurate prediction on a target language without ever using any annotated samples in that target language . Depending on the source and target language pairs , we reach performances in a zero-shot regime of up to 77 % of a model trained on target language data . Furthermore , we can increase this performance up to 87 % of a baseline model trained on target language data by performing cross-lingual learning from multiple source languages . In recent years , there has been an increasing interest in developing sentiment analysis models that predict sentiment at a more fine-grained level than at the level of a complete document . A paradigm coined as Aspect-based Sentiment Analysis ( ABSA ) addresses this need by defining the sentiment expressed in a text relative to an opinion target ( also called aspect ) . Consider the following example from a restaurant review : " Moules were excellent , lobster ravioli was VERY salty ! " In this example , there are two sentiment statements , one positive and one negative . The positive one is indicated by the word " excellent " and is expressed towards the opinion target " Moules " . The second , negative sentiment , is indicated by the word " salty " and is expressed towards the " lobster ravioli " . A key task within this fine-grained sentiment analysis consists of identifying so called opinion target expressions ( OTE ) . To automatically extract OTEs , supervised learning algorithms are usually employed which are trained on manually annotated corpora . In this paper , we are concerned with how to transfer classifiers trained on one domain to another domain . In particular , we focus on the transfer of models across languages to alleviate the need for multilingual training data . We propose a model that is capable of accurate zero-shot cross-lingual OTE extraction , thus reducing the reliance on annotated data for every language . Similar to Upadhyay et al . ( 2018 ) , our model leverages multilingual word embeddings ( Smith et al . , 2017 ; Lample et al . , 2018 ) that share a common vector space across various languages . The shared space allows us to transfer a model trained on source language data to predict OTEs in a target language for which no ( i.e. zero-shot setting ) or only small amounts of data are available , thus allowing to apply our model to under-resourced languages . Our main contributions can be summarized as follows : • We present the first approach for zero-shot cross-lingual opinion target extraction and achieve up to 87 % of the performance of a monolingual baseline . • We investigate the benefit of using multi-ple source languages for cross-lingual learning and show that we can improve by 6 to 8 points in F 1 -Score compared to a model trained on a single source language . • We investigate the benefit of augmenting the zero-shot approach with additional data points from the target language . We observe that we can save hundreds of annotated data points by employing a cross-lingual approach . • We compare two methods for obtaining cross-lingual word embeddings on the task . In this work , we presented a method for crosslingual and zero-shot extraction of opinion target expressions which we evaluated on 5 languages . Our approach uses multilingual word embeddings that are aligned into a single vector space to allow for cross-lingual transfer of models . Using English as a source language in a zeroshot setting , our approach was able to reach an F 1score of 0.50 for Spanish and 0.46 for Dutch . This corresponds to relative performances of 74 % and 77 % compared to a baseline system trained on target language data . By using multiple source languages , we increased the zero-shot performance to F 1 -scores of 0.58 and 0.53 , respectively , which correspond to 85 % and 87 % in relative terms . We investigated the benefit of augmenting the zeroshot approach with additional data points from the target language . Here , we observed that we can save several hundreds of annotated data points by employing a cross-lingual approach . Among the 5 considered languages , Turkish seemed to benefit the least from cross-lingual learning in all experiments . The reason for this might be that Turkish is the only agglutinative language in the dataset . Further , we compared two approaches for aligning multilingual word embeddings in a single vector space and found their results to vary for individual language pairs but to be comparable overall . Lastly , we compared our multilingual model with the state-of-the-art for all languages and saw that we achieve competitive performances for some languages and even present the best system for Russian and Turkish .

Challenge: Existing methods for opinion target expression recognition use supervised algorithms which require expensive manually annotated corpora unable to scale in languages and domains.
Approach: They propose to apply a zero-shot cross-lingual approach by training a convolutional neural network with multilingual word embeddings only on the source language.
Outcome: The proposed approach achieves up to 77% of the model trained on the target language and 87% when trained on multiple source languages.