Papers by Debanjan Mahata
On the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles (2021.naacl-main)
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| Challenge: | citation worthiness is an emerging research topic in the natural language processing domain . citation recommendation systems are often approached as ranking problems . |
| Approach: | They propose a hierarchical biLSTM-based model that uses two adjacent sentences to solve a citation worthiness problem. |
| Outcome: | The proposed approach can be applied to a dataset of over two million sentences and their labels. |
SNAP-BATNET: Cascading Author Profiling and Social Network Graphs for Suicide Ideation Detection on Social Media (N19-3)
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Rohan Mishra, Pradyumn Prakhar Sinha, Ramit Sawhney, Debanjan Mahata, Puneet Mathur, Rajiv Ratn Shah
| Challenge: | Suicide is a leading cause of death among youth worldwide and currently only uses text-based cues to detect suicidal ideation. |
| Approach: | They propose a deep learning based model to extract text-based features from tweets and a novel Feature Stacking approach to combine other community-based information. |
| Outcome: | The proposed model outperforms existing models on an annotated dataset of tweets using a three-phase strategy and proposes a novel Feature Stacking approach to combine other community-based information such as historical author profiling and graph embeddings. |
Learning Rich Representation of Keyphrases from Text (2022.findings-naacl)
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| Challenge: | Prior work has referred to extractive (part of document) or abstractive (not part of document). |
| Approach: | They propose to use a new pre-training objective to introduce keyphrases into transformer language models in discriminative and generative settings. |
| Outcome: | The proposed model improves performance in discriminative and generative settings and also improves on named entity recognition, question answering, relation extraction and abstractive summarization tasks. |
A Preliminary Exploration of GANs for Keyphrase Generation (2020.emnlp-main)
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| Challenge: | Existing studies on extractive keyphrases have shown promising results, but the results suggest that there is room for improvement. |
| Approach: | They propose a new keyphrase generation approach using Generative Adversarial Networks (GANs) their model produces a sequence of keyphrases and a discriminator distinguishes between human-curated and machine-generated keyphrase. |
| Outcome: | The proposed model outperforms the state-of-the-art generative models on benchmark datasets and is comparable to the best performing extractive models. |
An Annotated Dataset of Discourse Modes in Hindi Stories (2020.lrec-1)
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Swapnil Dhanwal, Hritwik Dutta, Hitesh Nankani, Nilay Shrivastava, Yaman Kumar, Junyi Jessy Li, Debanjan Mahata, Rakesh Gosangi, Haimin Zhang, Rajiv Ratn Shah, Amanda Stent
| Challenge: | Using a new corpus of sentences from Hindi short stories, we analyze the annotations for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. |
| Approach: | They propose to annotate sentences from Hindi short stories for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. |
| Outcome: | The proposed corpus has a high inter-annotator agreement (0.87 k-alpha) and is able to capture the nuances of the embedded discourse structures. |
GupShup: Summarizing Open-Domain Code-Switched Conversations (2021.emnlp-main)
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Laiba Mehnaz, Debanjan Mahata, Rakesh Gosangi, Uma Sushmitha Gunturi, Riya Jain, Gauri Gupta, Amardeep Kumar, Isabelle G. Lee, Anish Acharya, Rajiv Ratn Shah
| Challenge: | Abstractive summarization is the process of generating a condensed version of a given conversation while preserving the most salient aspects. |
| Approach: | They propose to use a dataset to analyze code-switched conversations in Hindi and English to summarize them. |
| Outcome: | The proposed dataset contains over 6,800 code-switched conversations and their corresponding human-annotated summaries in English (En) and Hi-En. |
#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media (P19-1)
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| Challenge: | a recent study has found that the disclosure of sexual abuse has positive psychological im- pacts. |
| Approach: | They propose to aggregate personal experiences of sexual harassment from Twitter posts to facilitate a better understanding of social media constructs and bring about social change. |
| Outcome: | The proposed model is compared with state-of-the-art models and is based on a three part Twitter-Specific Social Media Language Model. |
Two-Step Classification using Recasted Data for Low Resource Settings (2020.aacl-main)
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Shagun Uppal, Vivek Gupta, Avinash Swaminathan, Haimin Zhang, Debanjan Mahata, Rakesh Gosangi, Rajiv Ratn Shah, Amanda Stent
| Challenge: | Existing studies on NLP models focus on high resource languages like English, but there are only two datasets for Hindi. |
| Approach: | They propose a novel two-step classification method which uses textual-entailment predictions for classification task. |
| Outcome: | The proposed method improves classification performance by using a joint-objective for classification and textual entailment. |
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment (N19-3)
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| Challenge: | #MeToo movement provides platform to narrate personal experiences of sexual harassment. |
| Approach: | They propose a three-part ULMFiT architecture to tackle text subtleties in a classification task . they propose to annotate a manually annotated real-world dataset to test their approach . |
| Outcome: | The proposed model outperforms existing models that rely on handcrafted stylistic features and is more accurate than generic models. |
Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings (N18-2)
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| Challenge: | Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. |
| Approach: | They propose an unsupervised technique that leverages phrase embeddings for ranking keyphrases extracted from scientific articles using theme-weighted PageRank. |
| Outcome: | The proposed method performs better on benchmark datasets than other methods and is of high quality. |