Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction (2022)

Abstract: Target-oriented Opinion Words Extraction (TOWE) is a fine-grained sentiment analysis task that aims to extract the corresponding opinion words of a given opinion target from the sentence. Recently, deep learning approaches have made remarkable progress on this task. Nevertheless, the TOWE task still suffers from the scarcity of training data due to the expensive data annotation process. Limited labeled data increase the risk of distribution shift between test data and training data. In this paper, we propose exploiting massive unlabeled data to reduce the risk by increasing the exposure of the model to varying distribution shifts. Specifically, we propose a novel Multi-Grained Consistency Regularization (MGCR) method to make use of unlabeled data and design two filters specifically for TOWE to filter noisy data at different granularity. Extensive experimental results on four TOWE benchmark datasets indicate the superiority of MGCR compared with current state-of-the-art methods. The in-depth analysis also demonstrates the effectiveness of the different-granularity filters.

## introduction target-oriented opinion words extraction (towe) @xcite is an important subtask of aspect-based sentiment analysis (absa) @xcite , which aims to extract the corresponding opinion words for a given opinion target from the sentence. for the towe task, opinion targets, also called aspect terms, are the entities or objects in the sentence toward which users show attitudes; opinion words, sometimes known as opinion expressions, are those words explicitly mentioned in the sentence and used to express attitudes or opinions. figure 1 shows an example of the towe task. for the sentence "the dishes are amazingly delicious but the waiter is so rude.", the terms "dishes" and "waiter" are two opinion targets. the goal of towe is to extract "amazingly delicious" as the opinion words for the opinion target "dishes" and opinion word "rude" when given the opinion target "waiter". while seminal work casts towe as a sequence labeling problem, using fully supervised learning methods to identify opinion words and phrases from sentences, recent work shows that external sources of information can be highly useful for improving the performance. in particular, both syntactic knowledge @xcite @xcite and sentiment information @xcite have been exploited, with the former helping to identify the correlation between opinion targets and opinion expressions, and the latter helping to identify words and phrases that are correlated with sentiment polarities. existing work integrates these external features via representation structures @xcite @xcite and multi-task learning @xcite . intuitively, the goal of opinion words extraction is to obtain structured knowledge from raw data, and therefore ideally the amount of test data, namely the data from which opinion words are mined, should be large. this can inevitably increase the risk of distribution shift between test instances and the training data, even if the test data is from the same domain @xcite . existing work, however, adopt a supervised training setting, with the model being tuned on a set of fixed training data. to address this issue, we consider making use of raw text inputs to increase the exposure of the model to varying distribution shifts. our main idea is to (1) find raw data in a similar domain as the target-oriented opinion words extraction training data (i.e. gold data), (2) using a model trained on the gold data to assign silver labels, and then (3) design a set of filters to select the most useful set of silver data, so that (4) the selected silver data can be used together with the gold data for training a final model. as a pre-processing step, we train a opinion target extraction model, which is used to label the set of raw data. those raw sentences without any opinion target are filtered. the resulting raw data has the same format as the input structure of towe data. then in our implementation, steps (2) to ( 4 ) above are done in a joint batch training process. first, we initialize a towe model using bert. then in each batch, we randomly sample a subset of gold data and a subset of raw data, using the current model to assign pseudo labels to the raw data. according to the current model probabilities and an external sentiment classifier, we filter the silver data by removing low-confidence sentences, as well as masking low-confidence words from the remaining sentences. finally, the model parameters are updated by using the standard cross-entropy losses separately on both the gold and silver data, so that the next batch of training can start with the new model. the training process continues for a fixed number of iterations, and the model with the highest development scores are selected for testing. results on four standard benchmarks show that our method gives significantly improved results when raw data are used. we achieve the best reported results on all the datasets. in addition, the critical ablation demonstrates our sentencelevel and word-level noises filtering both bring improvements for the final results. in-depth analysis shows our method significantly reduces the extraction errors on different error types. to our knowledge, we are the first to consider the use of unlabeled data for towe, successfully giving state-of-the-art results on benchmarks. our code and datasets are available at https://github. com/towessl/towessl . ## method figure 3 shows the the framework of our multi-grained consistency regularization (mgcr) method. the towe model is a bert-based neural sequence labeling network. as mentioned in the introduction, we initialize a bert towe model (section 3.1), and then iteratively conduct batch training. in each batch, we impose a standard crossentropy loss on a set of sampled gold data (eq 5), and a regularization loss on a set of randomly sampled raw data with targets (eq 11). the latter is defined by using the current model to assign towe labels, and then adding a sentence-level (section 9) and a word-level (section 10) filter. besides, as mentioned in the introduction, mgcr exploits latent opinion words from a pre-trained review sentiment classification @xcite to filter noises more accurately. ## conclusion the towe task suffers from the risk of distribution shift which arises from scarce labeled data. in this paper, we propose the novel mgcr method to increase the exposure of the model to varying distribution shifts by exploiting unlabeled data, and naturally the risk can be reduced. in the mgcr method, two different-grained filters, i.e., sentencelevel (coarse-grained) and word-level (fine-grained) confidence-based thresholding, are designed to filter noisy sentences and words for the highquality exposure. to further underline the possible opinion words during learning, we employ a pre-trained sentiment classifier and incorporate sentiment-attention scores into the sentence-level filter. experimental results indicate that our mgcr method significantly outperforms all other towe methods and achieves state-of-the-art performance on four towe datasets. the in-depth analysis demonstrate the effectiveness of each component in mgcr.