GLUECoS : An Evaluation Benchmark for Code-Switched NLP

Khanuja, Simran; Dandapat, Sandipan; Srinivasan, Anirudh; Sitaram, Sunayana; Choudhury, Monojit

doi:10.48550/arxiv.2004.12376

Cited by 6 publications

(8 citation statements)

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“…This is also reflected in the semantic meaning of emoticons across different contexts, languages and cultures (Robertson et al, 2018). In multilingual settings, code-mixing of two languages in the same utterance (Khanuja et al, 2020), or borrowing a word from a different language due to influence from other languages, rather than internal changes in the same language. -Usage change, which is the local change of a word's nearest semantic neighbours from one meaning to another, as in the shift of word the "prison CELL to CELL phone" which is more of a cultural change than a semantic change (Hamilton et al, 2016a).…”

Section: Stance Contextmentioning

confidence: 99%

Capturing stance dynamics in social media: open challenges and research directions

Alkhalifa

Zubiaga

2022

Int J Digit Humanities

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Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest and impact. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets that cover short periods of time, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review of emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance in particular. We further discuss current directions in capturing stance dynamics in social media. We discuss the challenges encountered when dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence.

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Section: Stance Contextmentioning

confidence: 99%

Capturing stance dynamics in social media: open challenges and research directions

Alkhalifa

Zubiaga

2022

Int J Digit Humanities

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“…This is also reflected in the semantic meaning of emoticons across different contexts, languages and cultures [73]. In multilingual settings, code-mixing of two languages in the same utterance [46], or borrowing a word from a different language due to influence from other languages, rather than internal changes in the same language. -Usage change, which is the local change of a word's nearest semantic neighbours from one meaning to another, as in the shift of word the "prison CELL to CELL phone" which is more of a cultural change than a seman-tic change [39].…”

Section: Stance Contextmentioning

confidence: 99%

Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions

Alkhalifa¹,

Zubiaga²

2021

Preprint

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Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets with limited time coverage, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require in turn adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review in emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance particularly. We further discuss current directions in capturing stance dynamics in social media. We organise the challenges of dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence.

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“…There have been some research works in this direction, such as GLUECoS, an evaluation benchmark in code-mixed text [4], automatic word-level language identification for CMSM text [5,6], parsing pipeline for Hindi-English CMSM text [7,8], and POS tagging for CMSM text [9].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the FIRE 2020's Dravidian-CodeMix task 3 was devoted to codemixed sentiment analysis on Tamil and Malayalam languages. This task aims to classify the given CM youtube comments into one of the five predefined categories: positive, negative, mixed_feelings, not_<language> 4 , unknown_state.…”

Section: Introductionmentioning

confidence: 99%

CMSAOne@Dravidian-CodeMix-FIRE2020: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media Text

Dowlagar,

Mamidi

2021

Preprint

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Code-mixing(CM) is a frequently observed phenomenon that uses multiple languages in an utterance or sentence. CM is mostly practiced on various social media platforms and in informal conversations. Sentiment analysis (SA) is a fundamental step in NLP and is well studied in the monolingual text. Code-mixing adds a challenge to sentiment analysis due to its non-standard representations. This paper proposes a meta embedding with a transformer method for sentiment analysis on the Dravidian code-mixed dataset. In our method, we used meta embeddings to capture rich text representations. We used the proposed method for the Task: "Sentiment Analysis for Dravidian Languages in Code-Mixed Text", and it achieved an F1 score of 0.58 and 0.66 for the given Dravidian code mixed data sets. The code is provided in the Github https://github.com/suman101112/ fire-2020-Dravidian-CodeMix.

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GLUECoS : An Evaluation Benchmark for Code-Switched NLP

Cited by 6 publications

References 0 publications

Capturing stance dynamics in social media: open challenges and research directions

Capturing stance dynamics in social media: open challenges and research directions

Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions

CMSAOne@Dravidian-CodeMix-FIRE2020: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media Text

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