Towards Detection of Subjective Bias using Contextualized Word Embeddings

Pant, Kartikey; Dadu, Tanvi; Mamidi, Radhika

doi:10.1145/3366424.3382704

Cited by 16 publications

(17 citation statements)

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“…Most prior work on bias detection Fetahu, 2018, 2019;Pant et al, 2020) focus on predicting the presence of subjective bias in a sentence. We follow their setup.…”

Section: Binary Classificationmentioning

confidence: 99%

WIKIBIAS: Detecting Multi-Span Subjective Biases in Language

Yang

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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Biases continue to be prevalent in modern text and media, especially subjective bias -a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth. To tackle the problem of detecting and further mitigating subjective bias, we introduce a manually annotated parallel corpus WIKIBIAS with more than 4,000 sentence pairs from Wikipedia edits. This corpus contains annotations towards both sentencelevel bias types and token-level biased segments. We present systematic analyses of our dataset and results achieved by a set of state-ofthe-art baselines in terms of three tasks: bias classification, tagging biased segments, and neutralizing biased text. We find that current models still struggle with detecting multi-span biases despite their reasonable performances, suggesting that our dataset can serve as a useful research benchmark. We also demonstrate that models trained on our dataset can generalize well to multiple domains such as news and political speeches. 1

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“…Most prior work on bias detection Fetahu, 2018, 2019;Pant et al, 2020) focus on predicting the presence of subjective bias in a sentence. We follow their setup.…”

Section: Binary Classificationmentioning

confidence: 99%

WIKIBIAS: Detecting Multi-Span Subjective Biases in Language

Yang

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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“…tanvidadu (Dadu and Pant, 2020): Fine-tuned RoBERTa-large model (355 Million parameters with over a 50K vocabulary size) on response and its two immediate contexts. They reported results on three different types of inputs: response-only model, concatenation of immediate two context with response, and using an explicit separator token between the response and the final context.…”

Section: Kalaivania (Kalaivani a And D 2020)mentioning

confidence: 99%

“…Recently released models such as BERT and RoBERTa , exploit the use of pre-training and bidirectional transformers to enable efficient solutions obtaining state-of-the-art performance. Pre-trained embeddings significantly outperform the previous state-of-the-art in similar problems such as humor detection , and subjectivity detection (Pant et al, 2020).…”

Section: Introductionmentioning

confidence: 97%

Proceedings of the Second Workshop on Figurative Language Processing

2020

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Detecting sarcasm and verbal irony is critical for understanding people's actual sentiments and beliefs. Thus, the field of sarcasm analysis has become a popular research problem in natural language processing. As the community working on computational approaches for sarcasm detection is growing, it is imperative to conduct benchmarking studies to analyze the current state-of-the-art, facilitating progress in this area. We report on the shared task on sarcasm detection we conducted as a part of the 2nd Workshop on Figurative Language Processing (FigLang 2020) at ACL 2020. Referenceskalaivani A and Thenmozhi D. 2020. Sarcasm identification and detection in conversion context using BERT. In Proceedings of the Second Workshop on Figurative Language Processing,

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“…Recently released models such as BERT (Devlin et al, 2018) and RoBERTa (Liu et al, 2020), exploit the use of pre-training and bidirectional transformers to enable efficient solutions obtaining state-of-the-art performance. Pre-trained embeddings significantly outperform the previous state-of-the-art in similar problems such as humor detection (Weller and Seppi, 2019), and subjectivity detection (Pant et al, 2020).…”

Section: Introductionmentioning

confidence: 97%

Sarcasm Detection using Context Separators in Online Discourse

Dadu¹,

Pant²

2020

Proceedings of the Second Workshop on Figurative Language Processing

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Sarcasm is an intricate form of speech, where meaning is conveyed implicitly. Being a convoluted form of expression, detecting sarcasm is an assiduous problem. The difficulty in recognition of sarcasm has many pitfalls, including misunderstandings in everyday communications, which leads us to an increasing focus on automated sarcasm detection. In the second edition of the Figurative Language Processing (FigLang 2020) workshop, the shared task of sarcasm detection released two datasets, containing responses along with their context sampled from Twitter and Reddit.In this work, we use RoBERT a large to detect sarcasm in both the datasets. We further assert the importance of context in improving the performance of contextual word embedding based models by using three different types of inputs -Response-only, Context-Response, and Context-Response (Separated). We show that our proposed architecture performs competitively for both the datasets. We also show that the addition of a separation token between context and target response results in an improvement of 5.13% in the F1-score in the Reddit dataset.

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Towards Detection of Subjective Bias using Contextualized Word Embeddings

Cited by 16 publications

References 1 publication

WIKIBIAS: Detecting Multi-Span Subjective Biases in Language

WIKIBIAS: Detecting Multi-Span Subjective Biases in Language

Proceedings of the Second Workshop on Figurative Language Processing

Sarcasm Detection using Context Separators in Online Discourse

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