Papers by Smaranda Muresan

Metaphor Generation with Conceptual Mappings (2021.acl-long)

Challenge:	Existing approaches to authorship attribution model only learn from the output layer of pre-trained transformers, ignoring representations learned at other layers.
Approach:	They propose a model that leverages the various linguistic representations learned at different layers of pre-trained transformer-based models to model the authorship attribution task more effectively.
Outcome:	The proposed model performs better on out-of-domain and in-domain scenarios, while ignoring representations learned at other layers.

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Kevin Stowe, Tuhin Chakrabarty, Nanyun Peng, Smaranda Muresan, Iryna Gurevych

Semantic Text Processing

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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Tuhin Chakrabarty, Smaranda Muresan, Nanyun Peng

Text Generation

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.

Exploring Chain-of-Thought Reasoning for Steerable Pluralistic Alignment (2025.emnlp-main)

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Yunfan Zhang, Kathleen McKeown, Smaranda Muresan

Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels (D19-50)

Challenge:	Large Language Models (LLMs) are typically trained to reflect a relatively uniform set of values, which limits their applicability to tasks that require understanding of nuanced human perspectives.
Approach:	They propose to use Chain-of-Thought reasoning techniques to build steerable pluralistic models by fine-tuning on human-authored CoT and synthetic explanations.
Outcome:	The proposed methods outperform others and demonstrate strong sample efficiency.

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Tariq Alhindi, Jonas Pfeiffer, Smaranda Muresan

Text Classification

Challenge:	The system was evaluated on a unified development set without distributing the gold labels.
Approach:	They propose to use fine-grained propaganda detection to build models that can explain why an article is propagandistic.
Outcome:	The proposed model performed on all eighteen propaganda techniques in the corpus of the shared task.

Fine-tuned Language Models are Continual Learners (2022.emnlp-main)

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Thomas Scialom, Tuhin Chakrabarty, Smaranda Muresan

Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains (2021.acl-short)

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.

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Chenghao Yang, Yudong Zhang, Smaranda Muresan

DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking (2020.acl-main)

Challenge:	Among social media platforms, Reddit has emerged as the most promising one due to its anonymity and its focus on topic-based communities (subreddits) . a challenge for previous work on suicide risk assessment has been the small amount of labeled data.
Approach:	They propose to use social media to collect user data from r/SuicideWatch subreddit and annotate it with user-level suicide risk: no-risk, low-risk and high-risk.
Outcome:	The proposed model improves by using pseudo-labeling based on related issues around mental health (e.g., anxiety, depression)

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Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi, Siddharth Varia, Kriste Krstovski, Mona Diab, Smaranda Muresan

MorphAGram, Evaluation and Framework for Unsupervised Morphological Segmentation (2020.lrec-1)

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.

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Ramy Eskander, Francesca Callejas, Elizabeth Nichols, Judith Klavans, Smaranda Muresan

I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors (2023.findings-acl)

Challenge:	Unsupervised morphological segmentation is beneficial for many natural language processing tasks.
Approach:	They propose a framework for unsupervised morphological segmentation that uses Adaptor Grammars.
Outcome:	The proposed framework achieves state-of-the-art results across languages of different typologies, from fusional to polysynthetic and from high-resource to low-resourced.

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Tuhin Chakrabarty, Arkadiy Saakyan, Olivia Winn, Artemis Panagopoulou, Yue Yang, Marianna Apidianaki, Smaranda Muresan

Visual Data in NLP

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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Tuhin Chakrabarty, Arkadiy Saakyan, Smaranda Muresan

Multilinguality

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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Tuhin Chakrabarty, Aadit Trivedi, Smaranda Muresan

CONSISTENT: Open-Ended Question Generation From News Articles (2022.findings-emnlp)

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.

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Tuhin Chakrabarty, Justin Lewis, Smaranda Muresan

Text Generation

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.

A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis (2024.eacl-long)

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Robert Vacareanu, Siddharth Varia, Kishaloy Halder, Shuai Wang, Giovanni Paolini, Neha Anna John, Miguel Ballesteros, Smaranda Muresan

COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic (2021.acl-long)

Challenge:	Existing methods to improve few-shot performance in aspect-based sentiment analysis (ABSA) require complex interactions between the target and the polarity of the sentiment.
Approach:	They propose a pipeline approach to construct a noisy ABSA dataset and adapt it to the ABSA tasks.
Outcome:	The proposed model outperforms the state-of-the-art on the aspect extraction sentiment classification task and is capable of performing the harder aspect sentiment triplet extraction task.

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Arkadiy Saakyan, Tuhin Chakrabarty, Smaranda Muresan

Information Extraction & Text Mining

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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Tuhin Chakrabarty, Christopher Hidey, Smaranda Muresan

FeelingBlue: A Corpus for Understanding the Emotional Connotation of Color in Context (2023.tacl-1)

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.

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Amith Ananthram, Olivia Winn, Smaranda Muresan

Challenge:	Experimental results shed light on the emotional connotation of color in context . color is a powerful tool for conveying emotion across cultures .
Approach:	They propose a multimodal dataset for exploring the emotional connotation of color as mediated by line, stroke, texture, shape, and language.
Outcome:	The proposed model sheds light on the emotional connotation of color in context and the potential for future studies.

‘Lighter’ Can Still Be Dark: Modeling Comparative Color Descriptions (P18-2)

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Olivia Winn, Smaranda Muresan

Representation Learning

Challenge:	Multimodal approaches to object recognition ground adjectives and nouns from text using comparative adjectives.
Approach:	They propose a new paradigm of grounding comparative adjectives within the realm of color descriptions by using a vector model.
Outcome:	The proposed model generates representations of comparative adjectives with an average accuracy of 0.65 cosine similarity to the desired direction of change.

Figurative Language in Recognizing Textual Entailment (2021.findings-acl)

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Tuhin Chakrabarty, Debanjan Ghosh, Adam Poliak, Smaranda Muresan

BeSt: The Belief and Sentiment Corpus (2022.lrec-1)

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.

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Jennifer Tracey, Owen Rambow, Claire Cardie, Adam Dalton, Hoa Trang Dang, Mona Diab, Bonnie Dorr, Louise Guthrie, Magdalena Markowska, Smaranda Muresan, Vinodkumar Prabhakaran, Samira Shaikh, Tomek Strzalkowski

Connecting the Dots: Evaluating Abstract Reasoning Capabilities of LLMs Using the New York Times Connections Word Game (2024.emnlp-main)

Challenge:	a corpus of propositional content is a set of cognitive attitudes of different agents towards a text . propositional attitudes are a cognitive attitude, including belief and sentiment, towards .
Approach:	They propose a corpus which records cognitive state: who believes what, who has what sentiment . they use newswire and discussion forums in Chinese, English, and Spanish .
Outcome:	The proposed corpus records who believes what (i.e., factuality) and who has what sentiment towards what.

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Prisha Samdarshi, Mariam Mustafa, Anushka Kulkarni, Raven Rothkopf, Tuhin Chakrabarty, Smaranda Muresan

A Multi-layer Annotated Corpus of Argumentative Text: From Argument Schemes to Discourse Relations (L18-1)

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.

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Elena Musi, Manfred Stede, Leonard Kriese, Smaranda Muresan, Andrea Rocci

Challenge:	Recent interest in Argumentation Mining has brought to the fore the need for corpora annotated with argument information, which can be used as training data.
Approach:	They propose a set of guidelines for the annotation of argument schemes and a new annotation tool for the 'inferential' argument schemes.
Outcome:	The proposed corpus includes 112 argumentative microtexts and a new annotation tool.

AMPERSAND: Argument Mining for PERSuAsive oNline Discussions (D19-1)

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Tuhin Chakrabarty, Christopher Hidey, Smaranda Muresan, Kathy McKeown, Alyssa Hwang

NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation (2023.emnlp-main)

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.

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Oliver Li, Mallika Subramanian, Arkadiy Saakyan, Sky CH-Wang, Smaranda Muresan

Dialogue Systems & Conversational Agents

Challenge:	Social norms fundamentally shape interpersonal communication.
Approach:	They propose a human-in-the-loop pipeline to synthesize a bilingual dyadic dialogue dataset with turn-by-turn annotations of social norms for Chinese and American cultures.
Outcome:	The proposed dataset is high-quality through human evaluation and compares with existing models.

Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment (2023.emnlp-main)

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Sky CH-Wang, Arkadiy Saakyan, Oliver Li, Zhou Yu, Smaranda Muresan

Understanding Figurative Meaning through Explainable Visual Entailment (2025.naacl-long)

Challenge:	Current research on developing computational models of social norms has focused on American society.
Approach:	They propose to leverage a Chinese Q&A platform and a socialchiemistry dataset as proxies for contrasting cultural axes and align social situations cross-culturally.
Outcome:	The proposed model can reason across cultures using a Chinese Q&A platform and the existing socialChemistry dataset.

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Arkadiy Saakyan, Shreyas Kulkarni, Tuhin Chakrabarty, Smaranda Muresan

Visual Data in NLP

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.

Affective Idiosyncratic Responses to Music (2022.emnlp-main)

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Sky CH-Wang, Evan Li, Oliver Li, Smaranda Muresan, Zhou Yu

Multimodality

Challenge:	Affective responses to music are highly personal, but it's difficult to measure marginal effects of these variables . a study of 403M listener comments on a social music platform in china aims to address this gap .
Approach:	They propose to measure affective responses to music from 403M listener comments on a Chinese social music platform.
Outcome:	The proposed method identifies musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses from over 403M listener comments on a Chinese social music platform.

Towards Unsupervised Morphological Analysis of Polysynthetic Languages (2022.aacl-short)

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Sujay Khandagale, Yoann Léveillé, Samuel Miller, Derek Pham, Ramy Eskander, Cass Lowry, Richard Compton, Judith Klavans, Maria Polinsky, Smaranda Muresan

Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge (2020.acl-main)

Challenge:	Polysynthetic languages are low-resource, lacking large scale annotated datasets needed to build and/or evaluate computational models.
Approach:	They propose to use linguistic priors to help with morphological segmentation and part-of-speech tagging tasks for Adyghe and Inuktitut .
Outcome:	The proposed methods improve morphological segmentation and part-of-speech tagging tasks on Adyghe and Inuktitut.

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Tuhin Chakrabarty, Debanjan Ghosh, Smaranda Muresan, Nanyun Peng

Multitask Instruction-based Prompting for Fallacy Recognition (2022.emnlp-main)

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.

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Tariq Alhindi, Tuhin Chakrabarty, Elena Musi, Smaranda Muresan

Semantic Text Processing

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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Tuhin Chakrabarty, Xurui Zhang, Smaranda Muresan, Nanyun Peng

Commonsense Reasoning

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.

Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios (2020.emnlp-main)

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Ramy Eskander, Smaranda Muresan, Michael Collins

Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts (2024.findings-naacl)

Challenge:	a limited set of translations into one or more high-resource languages are available for POS tagging . a bi-LSTM architecture that uses contextualized word embeddings improves performance .
Approach:	They propose an unsupervised cross-lingual transfer approach for part-of-speech tagging . they use the Bible as parallel data to learn POS taggers for target languages .
Outcome:	The proposed approach improves accuracy on 12 diverse languages . the Bible is used as a parallel corpus for the study .

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Chenghao Yang, Tuhin Chakrabarty, Karli Hochstatter, Melissa Slavin, Nabila El-Bassel, Smaranda Muresan

Fact vs. Opinion: the Role of Argumentation Features in News Classification (2020.coling-main)

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.

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Tariq Alhindi, Smaranda Muresan, Daniel Preotiuc-Pietro

Text Classification

Challenge:	A 2018 study led by the Media Insight Project showed that most journalists think that their news organizations should clearly mark what is news reporting and what is commentary or opinion in order to combat fake news and gain public trust.
Approach:	They propose to classify news articles into newsstories and opinion pieces using models that aim to sup-plement the article content representation with argumentation features.
Outcome:	The proposed model outperforms linguistic features and improves on fine-tuned transformer-based models on data from publishers.

“Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space (2021.eacl-main)

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Debanjan Ghosh, Ritvik Shrivastava, Smaranda Muresan

Text Classification

Challenge:	Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved.
Approach:	They propose to use a corpus annotated with argumentative moves and sarcasm to model sarcastic relationships using deep learning architectures.
Outcome:	The proposed setup improves the argumentative relation classification task using deep learning architectures.

Emotion-Infused Models for Explainable Psychological Stress Detection (2021.naacl-main)

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Elsbeth Turcan, Smaranda Muresan, Kathleen McKeown

Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution (2025.coling-main)

Challenge:	a new study examines the use of emotion detection for detecting psychological stress in online posts . traditional multi-task learning and emotion-based language model fine-tuning are used to improve the model .
Approach:	They propose to use a semantically related task, emotion detection, for detecting psychological stress in online posts . they propose multi-task learning and emotion-based language model fine-tuning to improve the model .
Outcome:	The proposed model is more explainable and human-like than a black-box model . the proposed model mirrors psychological components of stress, the authors show .

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Milad Alshomary, Narutatsu Ri, Marianna Apidianaki, Ajay Patel, Smaranda Muresan, Kathleen McKeown

Explainability & Interpretability in NLP

Challenge:	Recent authorship attribution methods learn authorship representations of text in a latent, uninterpretable space, which hinders their usability in real-world applications.
Approach:	They propose a method for interpreting latent authorship representations by identifying representative points in the latent space and leveraging large language models to generate informative natural language descriptions of the writing style associated with each point.
Outcome:	The proposed method outperforms baseline methods on the authorship attribution task by +20% on average when aided with explanations from the method.

NORMSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly (2023.emnlp-main)

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Yi Fung, Tuhin Chakrabarty, Hao Guo, Owen Rambow, Smaranda Muresan, Heng Ji

Dialogue Systems & Conversational Agents

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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Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, Smaranda Muresan

To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging (2020.emnlp-main)

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.

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Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai, Smaranda Muresan, Jie Ma, Faisal Ladhak, Yaser Al-Onaizan

Learning to Follow Object-Centric Image Editing Instructions Faithfully (2023.findings-emnlp)

Challenge:	Using large amounts of unlabeled data to improve performance has become the foundation for many natural language processing tasks.
Approach:	They propose a task-specific semi-supervised approach that uses unlabeled data in a more task-agnostic manner.
Outcome:	The proposed approach achieves similar performance to BERT on a set of sequence tagging tasks with less financial and environmental impact.

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Tuhin Chakrabarty, Kanishk Singh, Arkadiy Saakyan, Smaranda Muresan

Visual Data in NLP

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.

Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction (2021.findings-acl)

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Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan

Low-Resource NLP

Challenge:	Detecting what emotions are expressed in text is a well-studied problem in natural language processing.
Approach:	They propose methods that combine common-sense knowledge with multi-task learning to perform joint emotion classification and emotion cause tagging.
Outcome:	The proposed models improve on both tasks when using common-sense reasoning and a multitask framework.

Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition (2024.findings-acl)

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Tariq Alhindi, Smaranda Muresan, Preslav Nakov

Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors (2021.findings-acl)

Challenge:	Existing work on fallacy recognition is still in its early stages, with limited datasets available.
Approach:	They propose to use GPT3.5 to generate synthetic examples and explore prompt settings to improve the representation of the infrequent classes.
Outcome:	The proposed model improves on existing models and generates synthetic examples with GPT3.5.

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Ramy Eskander, Cass Lowry, Sujay Khandagale, Francesca Callejas, Judith Klavans, Maria Polinsky, Smaranda Muresan

Browsing Lost Unformed Recollections: A Benchmark for Tip-of-the-Tongue Search and Reasoning (2025.acl-long)

Challenge:	Unsupervised morphological segmentation is an essential subtask in many natural language processing applications.
Approach:	They introduce two types of priors: grammar definition and linguist-provided affixes . they show that priors boost morphological segmentation performance in a minimally-supervised manner .
Outcome:	The proposed priors achieve 8.9% and 34.2% error reductions over the state-of-the-art unsupervised system.

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Sky CH-Wang, Darshan Girish Deshpande, Smaranda Muresan, Anand Kannappan, Rebecca Qian

Unsupervised Stem-based Cross-lingual Part-of-Speech Tagging for Morphologically Rich Low-Resource Languages (2022.naacl-main)

Challenge:	BLUR is a tip-of-the-tongue known-item search and reasoning benchmark for general AI assistants.
Approach:	They introduce a tip-of-the-tongue known-item search and reasoning benchmark for general AI assistants.
Outcome:	The proposed benchmark demands searching and reasoning across multimodal and multilingual inputs, as well as proficient tool use, in order to excel on.

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Ramy Eskander, Cass Lowry, Sujay Khandagale, Judith Klavans, Maria Polinsky, Smaranda Muresan