Introduction
In natural language, subjectivity refers to the aspects of communication used to express opinions, evaluations, and speculationsBIBREF0, often influenced by one's emotional state and viewpoints. Writers and editors of texts like news and textbooks try to avoid the use of biased language, yet subjective bias is pervasive in these texts. More than $56\%$ of Americans believe that news sources do not report the news objectively , thus implying the prevalence of the bias. Therefore, when presenting factual information, it becomes necessary to differentiate subjective language from objective language.
There has been considerable work on capturing subjectivity using text-classification models ranging from linguistic-feature-based modelsBIBREF1 to finetuned pre-trained word embeddings like BERTBIBREF2. The detection of bias-inducing words in a Wikipedia statement was explored in BIBREF1. The authors propose the "Neutral Point of View" (NPOV) corpus made using Wikipedia revision history, containing Wikipedia edits that are specifically designed to remove subjective bias. They use logistic regression with linguistic features, including factive verbs, hedges, and subjective intensifiers to detect bias-inducing words. In BIBREF2, the authors extend this work by mitigating subjective bias after detecting bias-inducing words using a BERT-based model. However, they primarily focused on detecting and mitigating subjective bias for single-word edits. We extend their work by incorporating multi-word edits by detecting bias at the sentence level. We further use their version of the NPOV corpus called Wiki Neutrality Corpus(WNC) for this work.
The task of detecting sentences containing subjective bias rather than individual words inducing the bias has been explored in BIBREF3. However, they conduct majority of their experiments in controlled settings, limiting the type of articles from which the revisions were extracted. Their attempt to test their models in a general setting is dwarfed by the fact that they used revisions from a single Wikipedia article resulting in just 100 instances to evaluate their proposed models robustly. Consequently, we perform our experiments in the complete WNC corpus, which consists of $423,823$ revisions in Wikipedia marked by its editors over a period of 15 years, to simulate a more general setting for the bias.
In this work, we investigate the application of BERT-based models for the task of subjective language detection. We explore various BERT-based models, including BERT, RoBERTa, ALBERT, with their base and large specifications along with their native classifiers. We propose an ensemble model exploiting predictions from these models using multiple ensembling techniques. We show that our model outperforms the baselines by a margin of $5.6$ of F1 score and $5.95\%$ of Accuracy.
Baselines and Approach
In this section, we outline baseline models like $BERT_{large}$. We further propose three approaches: optimized BERT-based models, distilled pretrained models, and the use of ensemble methods for the task of subjectivity detection.
Baselines and Approach ::: Baselines
FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently.
BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline.
BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset.
Baselines and Approach ::: Proposed Approaches
Optimized BERT-based models: We use BERT-based models optimized as in BIBREF6 and BIBREF7, pretrained on a dataset as large as twelve times as compared to $BERT_{large}$, with bigger batches, and longer sequences. ALBERT, introduced in BIBREF7, uses factorized embedding parameterization and cross-layer parameter sharing for parameter reduction. These optimizations have led both the models to outperform $BERT_{large}$ in various benchmarking tests, like GLUE for text classification and SQuAD for Question Answering.
Distilled BERT-based models: Secondly, we propose to use distilled BERT-based models, as introduced in BIBREF8. They are smaller general-purpose language representation model, pre-trained by leveraging distillation knowledge. This results in significantly smaller and faster models with performance comparable to their undistilled versions. We finetune these pretrained distilled models on the training corpus to efficiently detect subjectivity.
BERT-based ensemble models: Lastly, we use the weighted-average ensembling technique to exploit the predictions made by different variations of the above models. Ensembling methodology entails engendering a predictive model by utilizing predictions from multiple models in order to improve Accuracy and F1, decrease variance, and bias. We experiment with variations of $RoBERTa_{large}$, $ALBERT_{xxlarge.v2}$, $DistilRoBERTa$ and $BERT$ and outline selected combinations in tab:experimental-results.
Experiments ::: Dataset and Experimental Settings
We perform our experiments on the WNC dataset open-sourced by the authors of BIBREF2. It consists of aligned pre and post neutralized sentences made by Wikipedia editors under the neutral point of view. It contains $180k$ biased sentences, and their neutral counterparts crawled from $423,823$ Wikipedia revisions between 2004 and 2019. We randomly shuffled these sentences and split this dataset into two parts in a $90:10$ Train-Test split and perform the evaluation on the held-out test dataset.
For all BERT-based models, we use a learning rate of $2*10^{-5}$, a maximum sequence length of 50, and a weight decay of $0.01$ while finetuning the model. We use FastText's recently open-sourced automatic hyperparameter optimization functionality while training the model. For the BiLSTM baseline, we use a dropout of $0.05$ along with a recurrent dropout of $0.2$ in two 64 unit sized stacked BiLSTMs, using softmax activation layer as the final dense layer.
Experiments ::: Experimental Results
tab:experimental-results shows the performance of different models on the WNC corpus evaluated on the following four metrics: Precision, Recall, F1, and Accuracy. Our proposed methodology, the use of finetuned optimized BERT based models, and BERT-based ensemble models outperform the baselines for all the metrics.
Among the optimized BERT based models, $RoBERTa_{large}$ outperforms all other non-ensemble models and the baselines for all metrics. It further achieves a maximum recall of $0.681$ for all the proposed models. We note that DistillRoBERTa, a distilled model, performs competitively, achieving $69.69\%$ accuracy, and $0.672$ F1 score. This observation shows that distilled pretrained models can replace their undistilled counterparts in a low-computing environment.
We further observe that ensemble models perform better than optimized BERT-based models and distilled pretrained models. Our proposed ensemble comprising of $RoBERTa_{large}$, $ALBERT_{xxlarge.v2}$, $DistilRoBERTa$ and $BERT$ outperforms all the proposed models obtaining $0.704$ F1 score, $0.733$ precision, and $71.61\%$ Accuracy.
Conclusion
In this paper, we investigated BERT-based architectures for sentence level subjective bias detection. We perform our experiments on a general Wikipedia corpus consisting of more than $360k$ pre and post subjective bias neutralized sentences. We found our proposed architectures to outperform the existing baselines significantly. BERT-based ensemble consisting of RoBERTa, ALBERT, DistillRoBERTa, and BERT led to the highest F1 and Accuracy. In the future, we would like to explore document-level detection of subjective bias, multi-word mitigation of the bias, applications of detecting the bias in recommendation systems.

Question: Do the authors report only on English?
Answer: Unanswerable

Question: What is the baseline for the experiments?
Answer: FastText, BiLSTM, BERT

FastText, BERT , two-layer BiLSTM architecture with GloVe word embeddings

Question: Which experiments are perfomed?
Answer: They used BERT-based models to detect subjective language in the WNC corpus