Papers by Tuhin Chakrabarty
Metaphor Generation with Conceptual Mappings (2021.acl-long)
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| Challenge: | Existing models for metaphor generation lack conceptualization of meaning of the metaphors . recent neural models have led to advances in many areas of natural language generation . |
| Approach: | They propose to encode conceptual mappings between cognitive domains to generate metaphoric expressions by embedding verbs into a literal expression and deriving source/target pairs to train a controlled seq-to-seq generation model. |
| Outcome: | The proposed method outperforms existing models in automatic and human evaluations for basic metaphoricity and conceptual metaphor presence. |
Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation (2020.emnlp-main)
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| Challenge: | Literary tropes are at the crux of human imagination and communication. |
| Approach: | They propose to automatically transform similes from reddit to their literal counterparts using common sense knowledge to generate simile models. |
| Outcome: | The proposed method generates 88% novel similes that do not share properties with training data. |
It’s not Rocket Science: Interpreting Figurative Language in Narratives (2022.tacl-1)
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| Challenge: | Existing text representations by design rely on compositionality, while figurative language is often non-compositional. |
| Approach: | They propose to use a pre-trained language model to interpret figurative language types to adopt human strategies for interpreting figurativ language types: inferring meaning from context and relying on constituent words’ literal meanings. |
| Outcome: | The proposed models perform significantly worse than humans on discriminative and generative tasks, bridging the gap from human performance. |
Fine-tuned Language Models are Continual Learners (2022.emnlp-main)
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| Challenge: | Recent work on large language models relies on intuition that most tasks can be described via natural language instructions. |
| Approach: | They propose that a model should be able to keep extending its knowledge without forgetting previous skills. |
| Outcome: | The proposed model can learn 8 new diverse language generation tasks while maintaining good performance on previous tasks, spanning in total of 70 datasets. |
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking (2020.acl-main)
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Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi, Siddharth Varia, Kriste Krstovski, Mona Diab, Smaranda Muresan
| Challenge: | Fact Extraction and Verification datasets provide a resource for end-to-end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction. |
| Approach: | They propose a system that is resilient to attacks by multiple propositions, temporal reasoning, ambiguity and lexical variation and a sequence of evidence sentences and veracity relation predictions. |
| Outcome: | The proposed system is resilient to three realistic “attacks” and obtains state-of-the-art results due to improved evidence retrieval. |
I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors (2023.findings-acl)
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Tuhin Chakrabarty, Arkadiy Saakyan, Olivia Winn, Artemis Panagopoulou, Yue Yang, Marianna Apidianaki, Smaranda Muresan
| Challenge: | Visual metaphors are powerful rhetorical devices used to communicate creative ideas through images. |
| Approach: | They propose to generate visual metaphors from linguistic metaphors by using large language models and Diffusion models. |
| Outcome: | The proposed task requires the ability to model implicit meaning and compositionality. |
Don’t Go Far Off: An Empirical Study on Neural Poetry Translation (2021.emnlp-main)
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| Challenge: | despite improvements in machine translation quality, automatic poetry translation remains a challenging problem . et al., a study of automatic poetry translators shows that multilingual fine-tuning on poetic data outperforms bilingual fine-timing on non-poetic text . |
| Approach: | They propose to use poetic parallel corpora for 6 languages to study poetry translation . they find that multilingual fine-tuning on poetic data outperforms bilingual fine-uning . |
| Outcome: | The proposed model outperforms bilingual and multilingual models on poetic data . the proposed model is based on a parallel dataset of poetry translations for several languages . |
Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models (2021.emnlp-main)
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| Challenge: | In argumentation theory, an enthymeme is defined as incomplete argument found in discourse . encoding discourse-aware commonsense improves the quality of the generated implicit premises . |
| Approach: | They propose a task that generates an implicit premise in an enthymeme using commonsense . they use a narrative text dataset to analyze the quality of the generated premises . |
| Outcome: | The proposed model outperforms baseline models on three datasets. |
CONSISTENT: Open-Ended Question Generation From News Articles (2022.findings-emnlp)
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| Challenge: | Recent work on question generation has largely focused on factoid questions such as who, what, where, when about basic facts. |
| Approach: | They propose an end-to-end system for generating openended questions that are answerable from and faithful to the input text. |
| Outcome: | The proposed model outperforms existing models and can be used in news media organizations. |
COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic (2021.acl-long)
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| Challenge: | a new method for fact-checking is needed to detect disinformation on the web . a dataset COVID-Fact contains 4,086 claims concerning the COVId-19 pandemic . |
| Approach: | They propose a FEVER-like dataset COVID-Fact of 4,086 claims concerning the COVId-19 pandemic . they automatically detect true claims and their source articles and generate counter-claims using automatic methods . |
| Outcome: | The proposed method reduces the cost of building domain-specific datasets for detecting misinformation . the proposed dataset contains 4,086 claims concerning the COVID-19 pandemic . |
ENTRUST: Argument Reframing with Language Models and Entailment (2021.naacl-main)
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| Challenge: | Public opinion has been shown to be significantly influenced by framing effects. |
| Approach: | They propose a method for reframing arguments that combines controllable text generation with a post-decoding entailment component to achieve the same denotation. |
| Outcome: | The proposed method is effective compared to baselines along the dimensions of fluency, meaning, and trustworthiness/reduction of fear. |
Figurative Language in Recognizing Textual Entailment (2021.findings-acl)
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| Challenge: | Existing RTE models struggle to capture figurative language, despite its ubiquity, it remains a bottleneck in automatic text understanding. |
| Approach: | They propose to frame five existing figurative language datasets into over 12,500 RTE examples. |
| Outcome: | The proposed models struggle to perform pragmatic inference and reasoning about world knowledge. |
DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation (2021.naacl-demos)
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| Challenge: | Recent advances in transformer-based language models and their applications to dialogue systems have succeeded to generate fluent and human-like responses. |
| Approach: | They propose to leverage conversational lines as controllable and informative content-planning elements to guide the generation model produce engaging and informative responses. |
| Outcome: | The proposed system generates engaging and informative responses using convlines as controllable and informative content-planning elements. |
Connecting the Dots: Evaluating Abstract Reasoning Capabilities of LLMs Using the New York Times Connections Word Game (2024.emnlp-main)
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Prisha Samdarshi, Mariam Mustafa, Anushka Kulkarni, Raven Rothkopf, Tuhin Chakrabarty, Smaranda Muresan
| Challenge: | We evaluate the performance of large language models (LLMs) against expert and novice human players. |
| Approach: | They propose to use the New York Times Connections game as a test bed to evaluate the abstract reasoning capabilities of large language models (LLMs) they propose to test the ability of large-language models to be able to cluster and categorize words using semantic relations. |
| Outcome: | The proposed game is a test bed for evaluating abstract reasoning capabilities in humans and AI systems. |
Help me write a Poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing (2022.emnlp-main)
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| Challenge: | Recent work in training large language models to follow natural language instructions has opened up exciting opportunities for natural language interface design. |
| Approach: | They propose to train large language models to follow natural language instructions and to test whether LLMs improve the quality of the generated content. |
| Outcome: | The proposed system is competitive to publicly available LLMs trained on instructions and can satisfy unseen compositional instructions. |
Creative Natural Language Generation (2023.emnlp-tutorial)
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| Challenge: | This tutorial aims to bring awareness of the important and emerging research area of open-domain creative generation. |
| Approach: | They will review recent studies on creative language generation at sentence level as well as longer forms of text. |
| Outcome: | This paper reviews recent studies on creative language generation at sentence level as well as longer forms of text. |
Content Planning for Neural Story Generation with Aristotelian Rescoring (2020.emnlp-main)
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| Challenge: | Current approaches to narrative composition are plagued by difficulty in mastering structure, will veer between topics, and lack long-range cohesion. |
| Approach: | They propose a plot-generation language model and a set of rescoring models that implement an aspect of good story-writing as detailed in Aristotle's Poetics. |
| Outcome: | The proposed system improves the quality of the narrative generated from the proposed model and improves its relevance to a given prompt and quality of stories written with our principled plot structure. |
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions (D19-1)
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| Challenge: | Argument mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text. |
| Approach: | They propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level models of argumentation. |
| Outcome: | The proposed model improves on existing models using pointer networks and a pre-trained language model. |
IMHO Fine-Tuning Improves Claim Detection (N19-1)
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| Challenge: | Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. |
| Approach: | They propose to fine-tune a language model using a Reddit corpus of opinionated claims and use the internet acronyms IMO/IMHO to identify claims. |
| Outcome: | The proposed approach improves state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points. |
Understanding Figurative Meaning through Explainable Visual Entailment (2025.naacl-long)
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| Challenge: | Existing models for visual entailment and visual question-answering have limited ability to understand figurative meaning in images and captions. |
| Approach: | They propose a task framing the figurative meaning understanding problem as an explainable visual entailment task where the model has to predict whether the image entitles a caption and justify the predicted label with a textual explanation. |
| Outcome: | The proposed dataset contains 6,027 image, caption, label, explanation instances covering five diverse figurative phenomena. |
Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge (2020.acl-main)
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| Challenge: | Existing work on sarcasm generation focuses on context incongruity, but new work addresses this problem . |
| Approach: | They propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. |
| Outcome: | The proposed method generates sarcasm better than humans 34% of the time and better than a reinforced hybrid baseline 90% of the times. |
Multitask Instruction-based Prompting for Fallacy Recognition (2022.emnlp-main)
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| Challenge: | Fallacies are used as seemingly valid arguments to support a position and persuade the audience about its validity. |
| Approach: | They propose to use instruction-based prompting to recognize 28 unique fallacies across datasets . they also analyze the effect of model size and prompt choice on model performance . |
| Outcome: | The proposed approach can recognize 28 unique fallacies across domains and genres. |
MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding (2021.naacl-main)
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| Challenge: | a new method for generating metaphors is proposed to generate literal sentences . human evaluations show that our best model generates metaphors better than three well-crafted baselines 66% of the time on average. |
| Approach: | They propose a method to automatically construct a parallel corpus by transforming literal sentences to metaphorical ones using commonsense inference and masked language modeling. |
| Outcome: | The proposed method generates metaphors better than baselines 66% of the time on average. |
Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts (2024.findings-naacl)
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Chenghao Yang, Tuhin Chakrabarty, Karli Hochstatter, Melissa Slavin, Nabila El-Bassel, Smaranda Muresan
| Challenge: | Experimental results show that identifying the phases of opioid use disorder is highly contextual and challenging. |
| Approach: | They analyze 2500 opioid-related posts from various subreddits labeled with six different phases of opioid use . they annotate span-level extractive explanations and critically evaluate state-of-the-art models in a supervised, few-shot, or zero-shot setting. |
| Outcome: | The proposed models improve classification accuracy and quality of the extracted explanations. |
NORMSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly (2023.emnlp-main)
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| Challenge: | Existing methods to understand acceptable behavior have focused on a single culture and manually built datasets from non-conversational settings. |
| Approach: | They propose a framework to automatically extract culture-specific norms from multi-lingual conversations. |
| Outcome: | The proposed framework extracts culture-specific norms from multi-lingual conversations. |
FLUTE: Figurative Language Understanding through Textual Explanations (2022.emnlp-main)
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| Challenge: | Figurative language understanding is a recognizing textual entailment task, but lacks data for figurative language. |
| Approach: | They propose to use a dataset to analyze figurative NLI instances with explanations to improve models' performance. |
| Outcome: | The proposed dataset can scale up models even for figurative language using human annotations. |
Learning to Follow Object-Centric Image Editing Instructions Faithfully (2023.findings-emnlp)
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| Challenge: | avrahami et al., 2022b,a): natural language instructions are often underspecified, requiring models to uncover their implicit meaning. |
| Approach: | They propose to use paired data to model the implicit meaning of instructions . they also propose to ground the model to localize where the edit has to be performed . |
| Outcome: | The proposed model performs better than state-of-the-art baselines on paired data, showing improvements in quality and faithfulness. |