Debanjan Ghosh


2021

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“Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space
Debanjan Ghosh | Ritvik Shrivastava | Smaranda Muresan
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. Users often use figurative language, such as sarcasm, either as persuasive devices or to attack the opponent by an ad hominem argument. To further our understanding of the role of sarcasm in shaping the disagreement space, we present a thorough experimental setup using a corpus annotated with both argumentative moves (agree/disagree) and sarcasm. We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e.g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures). We demonstrate that modeling sarcasm improves the argumentative relation classification task (agree/disagree/none) in all setups.

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“Sharks are not the threat humans are”: Argument Component Segmentation in School Student Essays
Tariq Alhindi | Debanjan Ghosh
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications

Argument mining is often addressed by a pipeline method where segmentation of text into argumentative units is conducted first and proceeded by an argument component identification task. In this research, we apply a token-level classification to identify claim and premise tokens from a new corpus of argumentative essays written by middle school students. To this end, we compare a variety of state-of-the-art models such as discrete features and deep learning architectures (e.g., BiLSTM networks and BERT-based architectures) to identify the argument components. We demonstrate that a BERT-based multi-task learning architecture (i.e., token and sentence level classification) adaptively pretrained on a relevant unlabeled dataset obtains the best results.

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Figurative Language in Recognizing Textual Entailment
Tuhin Chakrabarty | Debanjan Ghosh | Adam Poliak | Smaranda Muresan
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Proceedings of the Second Workshop on Figurative Language Processing
Beata Beigman Klebanov | Ekaterina Shutova | Patricia Lichtenstein | Smaranda Muresan | Chee Wee | Anna Feldman | Debanjan Ghosh
Proceedings of the Second Workshop on Figurative Language Processing

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A Report on the 2020 Sarcasm Detection Shared Task
Debanjan Ghosh | Avijit Vajpayee | Smaranda Muresan
Proceedings of the Second Workshop on Figurative Language Processing

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.

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Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge
Tuhin Chakrabarty | Debanjan Ghosh | Smaranda Muresan | Nanyun Peng
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-and-edit framework to instantiate two major characteristics of sarcasm: reversal of valence and semantic incongruity with the context, which could include shared commonsense or world knowledge between the speaker and the listener. While prior works on sarcasm generation predominantly focus on context incongruity, we show that combining valence reversal and semantic incongruity based on the commonsense knowledge generates sarcasm of higher quality. Human evaluation shows that our system generates sarcasm better than humans 34% of the time, and better than a reinforced hybrid baseline 90% of the time.

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Interpreting Verbal Irony: Linguistic Strategies and the Connection to theType of Semantic Incongruity
Debanjan Ghosh | Elena Musi | Smaranda Muresan
Proceedings of the Society for Computation in Linguistics 2020

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An Exploratory Study of Argumentative Writing by Young Students: A transformer-based Approach
Debanjan Ghosh | Beata Beigman Klebanov | Yi Song
Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

We present a computational exploration of argument critique writing by young students. Middle school students were asked to criticize an argument presented in the prompt, focusing on identifying and explaining the reasoning flaws. This task resembles an established college-level argument critique task. Lexical and discourse features that utilize detailed domain knowledge to identify critiques exist for the college task but do not perform well on the young students’ data. Instead, transformer-based architecture (e.g., BERT) fine-tuned on a large corpus of critique essays from the college task performs much better (over 20% improvement in F1 score). Analysis of the performance of various configurations of the system suggests that while children’s writing does not exhibit the standard discourse structure of an argumentative essay, it does share basic local sequential structures with the more mature writers.

2018

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Sarcasm Analysis Using Conversation Context
Debanjan Ghosh | Alexander R. Fabbri | Smaranda Muresan
Computational Linguistics, Volume 44, Issue 4 - December 2018

Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, the speaker’s sarcastic intent is not always apparent without additional context. Focusing on social media discussions, we investigate three issues: (1) does modeling conversation context help in sarcasm detection? (2) can we identify what part of conversation context triggered the sarcastic reply? and (3) given a sarcastic post that contains multiple sentences, can we identify the specific sentence that is sarcastic? To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the current turn. We show that LSTM networks with sentence-level attention on context and current turn, as well as the conditional LSTM network, outperform the LSTM model that reads only the current turn. As conversation context, we consider the prior turn, the succeeding turn, or both. Our computational models are tested on two types of social media platforms: Twitter and discussion forums. We discuss several differences between these data sets, ranging from their size to the nature of the gold-label annotations. To address the latter two issues, we present a qualitative analysis of the attention weights produced by the LSTM models (with attention) and discuss the results compared with human performance on the two tasks.

2017

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The Role of Conversation Context for Sarcasm Detection in Online Interactions
Debanjan Ghosh | Alexander Richard Fabbri | Smaranda Muresan
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, speaker’s sarcastic intent is not always obvious without additional context. Focusing on social media discussions, we investigate two issues: (1) does modeling of conversation context help in sarcasm detection and (2) can we understand what part of conversation context triggered the sarcastic reply. To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the sarcastic response. We show that the conditional LSTM network (Rocktäschel et al. 2015) and LSTM networks with sentence level attention on context and response outperform the LSTM model that reads only the response. To address the second issue, we present a qualitative analysis of attention weights produced by the LSTM models with attention and discuss the results compared with human performance on the task.

2016

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Coarse-grained Argumentation Features for Scoring Persuasive Essays
Debanjan Ghosh | Aquila Khanam | Yubo Han | Smaranda Muresan
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Towards Feasible Guidelines for the Annotation of Argument Schemes
Elena Musi | Debanjan Ghosh | Smaranda Muresan
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

2015

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Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words
Debanjan Ghosh | Weiwei Guo | Smaranda Muresan
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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Analyzing Argumentative Discourse Units in Online Interactions
Debanjan Ghosh | Smaranda Muresan | Nina Wacholder | Mark Aakhus | Matthew Mitsui
Proceedings of the First Workshop on Argumentation Mining

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Annotating Multiparty Discourse: Challenges for Agreement Metrics
Nina Wacholder | Smaranda Muresan | Debanjan Ghosh | Mark Aakhus
Proceedings of LAW VIII - The 8th Linguistic Annotation Workshop

2012

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Relation Classification using Entity Sequence Kernels
Debanjan Ghosh | Smaranda Muresan
Proceedings of COLING 2012: Posters

2011

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Using Sequence Kernels to identify Opinion Entities in Urdu
Smruthi Mukund | Debanjan Ghosh | Rohini Srihari
Proceedings of the Fifteenth Conference on Computational Natural Language Learning

2010

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Using Cross-Lingual Projections to Generate Semantic Role Labeled Annotated Corpus for Urdu - A Resource Poor Language
Smruthi Mukund | Debanjan Ghosh | Rohini Srihari
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)