Papers by Sudipta Kar
Learning to Retrieve Engaging Follow-Up Queries (2023.findings-eacl)
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Christopher Richardson, Sudipta Kar, Anjishnu Kumar, Anand Ramachandran, Zeynab Raeesy, Omar Khan, Abhinav Sethy
| Challenge: | Open domain conversational agents can answer a wide range of targeted queries, but knowledge exploration is a lengthy task. |
| Approach: | They propose a retrieval based system for predicting the next questions that the user might have . they train ranking models on a dataset called the Follow-up Query Bank . |
| Outcome: | The proposed system can proactively assist users in knowledge exploration leading to a more engaging dialog. |
Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues (2020.lrec-1)
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| Challenge: | Using the MPAA rating, movie content can negatively affect children’s behaviour, for example, watching specific programs may encourage irresponsible sexual behavior and alcohol usage in teenagers. |
| Approach: | They propose an RNN-based architecture that jointly models the genre and the emotions in the script to predict the MPAA rating. |
| Outcome: | The proposed model outperforms the traditional machine learning method by 7% and achieves an 81% weighted F1 score. |
Folksonomication: Predicting Tags for Movies from Plot Synopses using Emotion Flow Encoded Neural Network (C18-1)
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| Challenge: | Existing systems that generate tags for movies can help users better retrieve movies based on their personal preferences and user profiles. |
| Approach: | They propose a neural network model that merges synopses and emotion flows to predict a set of movies' tags. |
| Outcome: | The proposed model outperforms a machine learning system by learning 18% more tags than the previous one. |
MultiCoNER: A Large-scale Multilingual Dataset for Complex Named Entity Recognition (2022.coling-1)
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| Challenge: | Named Entity Recognition (NER) is a core task in Natural Language Processing. |
| Approach: | They present a large multilingual dataset for Named Entity Recognition that covers 3 domains across 11 languages and multilingual and code-mixing subsets. |
| Outcome: | The proposed dataset is large and multilingual, covering 11 languages and subsets. |
LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation (2020.lrec-1)
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| Challenge: | Recent trends in NLP research have raised an interest in linguistic code-switching . however, many of these approaches are limited to a few language pairs and a specific domain . |
| Approach: | They propose a centralized benchmark for Linguistic Code-switching Evaluation that combines eleven corpora covering four different code-switch languages and four tasks. |
| Outcome: | The proposed benchmark provides a centralized benchmark and compares with other benchmarks in real-time. |
BanFakeNews: A Dataset for Detecting Fake News in Bangla (2020.lrec-1)
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| Challenge: | Impact of fake news is creating havoc worldwide. |
| Approach: | They propose an annotated dataset of 50K news that can be used for building automated fake news detection systems for a low resource language like Bangla. |
| Outcome: | The proposed system can be built with state-of-the-art NLP techniques for a low resource language like Bangla. |
MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a core task in Natural Language Processing. |
| Approach: | They present a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages in monolingual and multilingual settings. |
| Outcome: | The proposed dataset covers 33 entity classes across 12 languages in monolingual and multilingual settings. |
SentNoB: A Dataset for Analysing Sentiment on Noisy Bangla Texts (2021.findings-emnlp)
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| Challenge: | Bangla is the sixth most spoken language worldwide and the second Indo-Aryan language after Hindi. |
| Approach: | They propose an annotated sentiment analysis dataset made of informally written Bangla texts. |
| Outcome: | The proposed dataset is compared with neural networks and pretrained models . it shows that hand-crafted lexical features provide superior performance than neural networks . |
Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books (N18-2)
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| Challenge: | We obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |
| Approach: | They propose to model the flow of emotions over a book using recurrent neural networks and quantify its usefulness in predicting success in books. |
| Outcome: | The proposed model obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |
Multi-view Story Characterization from Movie Plot Synopses and Reviews (2020.emnlp-main)
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| Challenge: | Existing methods for characterizing stories by generating tags from synopses suffer from coverage issues. |
| Approach: | They propose to use synopses and reviews to characterize stories by inferring attributes such as theme and style from written synopsis and reviews. |
| Outcome: | The proposed model improves over methods that only use synopses and reviews . it can extract a complementary set of story attributes from reviews without supervision . |
MPST: A Corpus of Movie Plot Synopses with Tags (L18-1)
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| Challenge: | a corpus of movie plot synopses and tags can be used to build automatic tagging systems . a method to collect these tags allows us to learn to predict tags from plot synoopsis . |
| Approach: | They propose to collect a corpus of movie plot synopses and 70 tags to analyze their properties. |
| Outcome: | The proposed method can be used to predict movie tags from plot synopses. |