Papers by Pruthwik Mishra

6 papers
Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages (2024.naacl-srw)

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Challenge: Named Entity Recognition (NER) is a useful component in NLP applications.
Approach: They propose to use annotated named entity corpora to classify a given entity into a category within a textual document.
Outcome: The proposed model achieves an F1 score of 0.80 on an unseen dataset for Indian languages.
Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects - A Survey (2024.findings-acl)

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Challenge: scholarly attention has turned to the development of text summarization methods that are more closely tailored and controlled to align with specific objectives and user needs.
Approach: They formalize a controllable text summarization task and categorize controllability attributes according to their shared characteristics and objectives.
Outcome: The proposed method is tailored to meet the specific intent and needs of users.
Linguistically Informed Hindi-English Neural Machine Translation (2020.lrec-1)

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Challenge: Neural Machine Translation (NMT) is a promising approach to machine translation . lack of parallel training data for Hindi-English is limiting .
Approach: They propose to incorporate linguistic knowledge encoded by Hindi phenomena into a Transformer model to improve the translation performance.
Outcome: The proposed model incorporates linguistic features to improve the translation performance.
AGIC: Attention-Guided Image Captioning to Improve Caption Relevance (2026.findings-eacl)

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Challenge: Existing methods for image captioning generate generic captions that are limited in capturing nuanced visual details.
Approach: They propose attention-guided image captioning which amplifies visual regions directly in the feature space to guide caption generation.
Outcome: The proposed approach matches or surpasses state-of-the-art models while achieving faster inference.
HAWP: a Dataset for Hindi Arithmetic Word Problem Solving (2022.lrec-1)

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Challenge: Word problem solving is a challenging and interesting task in NLP.
Approach: They propose to use equations to solve Hindi arithmetic word problems . they propose to also use equation equivalence to evaluate word problem solvers .
Outcome: The proposed dataset is based on 2336 arithmetic word problems in Hindi . it also includes baseline systems and evaluation techniques .
Annotated Corpus for Sentiment Analysis in Odia Language (2020.lrec-1)

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Challenge: Existing sentiment analysis models are not available for Odia 1 as it is a resource-poor language.
Approach: They create an annotated Odia corpus and test its usability by training and testing on the corpus using various classifiers.
Outcome: The created corpus contains 2045 Odia sentences from news domain annotated with sentiment labels using a well-defined annotation scheme.

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