Papers by Snigdha Chaturvedi
Unsupervised Extractive Opinion Summarization Using Sparse Coding (2022.acl-long)
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| Challenge: | Existing methods for opinion summarization rely on human annotations, which may not be feasible. |
| Approach: | They propose to perform opinion summarization in an unsupervised manner by using a dictionary learning algorithm that implicitly captures semantic information from the review text. |
| Outcome: | The proposed algorithm performs well on SPACE and AMAZON datasets and performs controllable summarization to generate aspect-specific summaries using only a few samples. |
Dual Process Masking for Dialogue Act Recognition (2024.findings-emnlp)
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Yeo Jin Kim, Halim Acosta, Wookhee Min, Jonathan Rowe, Bradford Mott, Snigdha Chaturvedi, James Lester
| Challenge: | Dialogue act recognition is the task of classifying conversational utterances based on their communicative intent or function. |
| Approach: | They propose a dual-processing approach that masks less important tokens in the input and enhances interpretability by using the masks applied during classification learning. |
| Outcome: | The proposed approach significantly improves performance over strong baselines for dialogue act recognition on a collaborative problem-solving dataset and three public dialogue benchmarks. |
Uncovering Implicit Gender Bias in Narratives through Commonsense Inference (2021.findings-emnlp)
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| Challenge: | Pre-trained language models learn harmful biases from their training corpora and may repeat these biase if used for generation. |
| Approach: | They focus on gender biases associated with the protagonist in model-generated stories and use a commonsense reasoning engine to uncover them. |
| Outcome: | The proposed model-generated stories are based on a commonsense reasoning engine and are able to uncover gender biases in the protagonist's motivations, attributes, mental states, and implications on others. |
Adversarial Scrubbing of Demographic Information for Text Classification (2021.emnlp-main)
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Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li, Junier Oliva, Shashank Srivastava, Snigdha Chaturvedi
| Challenge: | Existing frameworks to debias contextual representations can encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated task. |
| Approach: | They propose an adversarial learning framework to debias contextual representations by encoding undesirable attributes while being trained for an unrelated task. |
| Outcome: | The proposed framework debiases representations on 8 datasets while remaining informative on the target task. |
Towards Inter-character Relationship-driven Story Generation (2022.emnlp-main)
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| Challenge: | Recent story generation methods can generate stories based on open-ended prompts and planners but can neither encode character relationships nor give explicit control over the characters and their relationships. |
| Approach: | They propose a model that uses relationships as latent variables for story generation and propose 'relationship-driven' story generation. |
| Outcome: | The proposed model generates stories sentence by sentence with relationships that are more faithful to desired relationships while maintaining the content quality. |
Curricular Next Conversation Prediction Pretraining for Transcript Segmentation (2023.findings-eacl)
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| Challenge: | Prior research on document segmentation has focused on segmenting documents such as Wikipedia articles. |
| Approach: | They propose to pretrain a model to identify consecutive conversations to address these challenges . they introduce a curriculum to Advanced NCP to make the task more relevant to the downstream task . |
| Outcome: | The proposed model outperforms previous models in speech recognition errors and is robust to speech recognition. |
How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation? (2021.acl-short)
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| Challenge: | Existing approaches to Table-to-Text generation suffer from issues such as missing information, repetition and repetition. |
| Approach: | They propose to use Inverse Reinforcement Learning (IRL) to solve the Table-to-Text task . they use multiple interpretable unsupervised reward components that are combined linearly to form a composite reward function. |
| Outcome: | The proposed task outperforms strong RL baselines marginally in the Table-to-Text task. |
Learning Fair Representations via Rate-Distortion Maximization (2022.tacl-1)
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| Challenge: | Empirical evaluations show that FaRM debiases representations with or without a target task at hand. |
| Approach: | They propose a method that makes representations belonging to the same protected attribute class uncorrelated, using the rate-distortion function. |
| Outcome: | Empirical results show that the proposed technique achieves state-of-the-art performance on several datasets and leaks significantly less protected attribute information against an attack by a non-linear probing network. |
Returning to the Start: Generating Narratives with Related Endpoints (2024.naacl-short)
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| Challenge: | RENarGen generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
| Approach: | They propose a novel novel novel that generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
| Outcome: | The proposed paradigm generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
SocialGaze: Improving the Integration of Human Social Norms in Large Language Models (2024.findings-emnlp)
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| Challenge: | Increasingly, large language models (LLMs) are able to understand and rationalize socially acceptable behaviors, but they are often misaligned with human consensus. |
| Approach: | They propose a multi-step prompting framework that verbalizes a social situation from multiple perspectives before forming a judgment. |
| Outcome: | The proposed framework improves the alignment with human judgments by up to 11 F1 points with the GPT-3.5 model. |
Improving Classroom Dialogue Act Recognition from Limited Labeled Data with Self-Supervised Contrastive Learning Classifiers (2023.findings-acl)
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| Challenge: | Recognizing classroom dialogue acts has significant promise for yielding insight into teaching, student learning, and classroom dynamics. |
| Approach: | They propose to use a contrastive learning-based self-supervised approach to improve classroom dialogue act recognition from limited labeled data by increasing the accuracy of dialogue act recognization and minimizing embedding distance between the same dialogue acts. |
| Outcome: | The proposed model outperforms baseline models when trained with limited examples per dialogue act and outperformed other few-shot models that require considerably more labeled data. |
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization (2022.findings-emnlp)
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| Challenge: | Existing studies focus on summarizing news documents or structured documents. |
| Approach: | They propose to use a large-scale narrative summarization dataset to encourage research . they find there is a performance gap between humans and the models on NarraSum . |
| Outcome: | The proposed dataset shows that humans and state-of-the-art models perform poorly when summarizing a narrative . it contains 122K narratives collected from synopses of movies and TV episodes with diverse genres . |
Coverage-based Fairness in Multi-document Summarization (2025.naacl-long)
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| Challenge: | Existing studies quantify summary-level fairness using Proportional Representation, but they ignore corpus-level unfairness. |
| Approach: | They propose a new summary-level fairness measure that considers redundancy in documents . they evaluate the fairness of thirteen different multi-document summarization systems . |
| Outcome: | The proposed measure is based on coverage of documents with different social attribute values and considers redundancy within documents. |
Cue Me In: Content-Inducing Approaches to Interactive Story Generation (2020.aacl-main)
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| Challenge: | Existing methods for automatic story generation focus on one-shot generation, but we focus on interactive story generation. |
| Approach: | They propose two ways to incorporate user-provided cue phrases into automatic story generation. |
| Outcome: | The proposed approach produces more topically coherent and personalized stories than baseline methods. |
Unsupervised Opinion Summarization Using Approximate Geodesics (2023.findings-emnlp)
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| Challenge: | Existing methods for opinion summarization are limited due to the scarcity of data. |
| Approach: | They propose a system to perform unsupervised extractive opinion summarization using a dictionary-based representation learning model that generates topical representations of texts. |
| Outcome: | The proposed system achieves strong performance on three opinion summarization datasets. |
PARROT: Zero-Shot Narrative Reading Comprehension via Parallel Reading (2023.findings-emnlp)
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| Challenge: | Existing approaches to narrative comprehension require extensive annotation of data. |
| Approach: | They propose a zero-shot approach for narrative comprehension through parallel reading using two parallel narratives that tell the same story. |
| Outcome: | The proposed approach surpasses previous zero-shot approaches and comparable performance to fully supervised models. |
Exploring Safety-Utility Trade-Offs in Personalized Language Models (2025.naacl-long)
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| Challenge: | Prior studies have shown that large language models can exhibit bias against specific demographic groups and engage in the generation of stereotypical responses. |
| Approach: | They propose a framework to evaluate LLM performance along two axes: safety and utility. |
| Outcome: | The proposed framework evaluates the performance of LLMs along two axes: safety and utility. |
Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation (2022.findings-emnlp)
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| Challenge: | Large pre-trained language models have enabled open-ended generation frameworks to tackle a variety of tasks beyond data-to-text generation. |
| Approach: | They propose a new task to generate a factual description about an entity given guiding keys and grounding passages using a dataset. |
| Outcome: | The proposed model improves factual correctness and recall significantly compared to previous models. |
Where Have I Heard This Story Before? Identifying Narrative Similarity in Movie Remakes (N18-2)
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| Challenge: | Existing methods to identify instances of similar narratives are limited by annotated data. |
| Approach: | They propose a task for identifying instances of similar narratives from a collection of narrative texts. |
| Outcome: | The proposed approach yields an 8% absolute improvement over a baseline on a novel dataset of plot summaries of 577 movie remakes from Wikipedia. |
CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories (2026.acl-long)
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| Challenge: | Increasing numbers of authors are using AI to assist in the process of writing stories. |
| Approach: | They analyze 8 category-pairs of character that assess how characters are portrayed in short stories . they find similarities between LLMs and human-written stories based on categories . |
| Outcome: | The analysis includes questions on popular LLMs and recently published human-written stories. |
Classifying Unreliable Narrators with Large Language Models (2025.acl-long)
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| Challenge: | a recent study identifies unreliable narrators, i.e. those who unintentionally misrepresent information . authors propose using computational methods to identify unredependable narrators . adbrei: readers implicitly question the reliability of the nrator . |
| Approach: | They propose using computational methods to identify unreliable narrators . they use literary theory to define different types of unredependable narrators . |
| Outcome: | The proposed method can identify unreliable narrators on real-world text data. |
Modeling Protagonist Emotions for Emotion-Aware Storytelling (2020.emnlp-main)
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| Challenge: | Cognitive scientists have pinpointed the central role of emotions in storytelling. |
| Approach: | They propose to use Emotion Supervision and two Emotion-Reinforced models to generate stories that follow the desired emotion arcs for the protagonist. |
| Outcome: | The proposed models generate stories that follow the desired emotion arcs without sacrificing story quality. |
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences (N18-1)
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| Challenge: | Using a dataset of 6,500+ questions, we found that human solvers achieved an F1-score of 88.1%. |
| Approach: | They propose a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. |
| Outcome: | The proposed reading comprehension challenge is based on a reading comprehension dataset with 6,500+ questions and 1000+ paragraphs across 7 domains. |
Improving Fairness of Large Language Models in Multi-document Summarization (2025.acl-short)
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| Challenge: | Recent studies focus on summary-level fairness, while corpus-level focuses on corpus of summaries. |
| Approach: | They propose a preference tuning method that focuses on both summary-level and corpus-level fairness in MDS. |
| Outcome: | The proposed method outperforms baselines while maintaining critical qualities of summaries. |
Rationale-based Opinion Summarization (2024.naacl-long)
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| Challenge: | Existing methods to generate concise summaries of reviews are generic and lack supporting details. |
| Approach: | They propose a rationale-based opinion summarization paradigm that outputs representative opinions and corresponding rationales. |
| Outcome: | The proposed method is more useful than conventional summarizations. |
Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts (2020.acl-main)
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| Challenge: | Existing methods to diagnose depression require time-intensive interviews, assessments, and analysis. |
| Approach: | They propose a model that analyzes interview transcripts to identify depression while jointly categorizing interview prompts into latent categories. |
| Outcome: | The proposed model outperforms baseline models and provides psycholinguistic insights about depression. |
Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations? (2026.acl-long)
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| Challenge: | Power differences shape human communication through well-documented socio-cognitive effects . asymmetric relationships or power differentials give rise to well-known socio-computational effects - lianelli, 1976 . |
| Approach: | They simulate multi-turn, power-asymmetric dialogues with personas from diverse professions . they find that LLMs show key socio-cognitive effects of power, albeit with nuances and variability . |
| Outcome: | The results show that large language models exhibit socio-cognitive effects of power . the results are consistent with previous studies on LLMs . |
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization (2022.findings-acl)
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Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, Snigdha Chaturvedi
| Challenge: | Existing methods for extracting multi-document news summarization neglect relative importance of documents. |
| Approach: | They propose to concatenate all documents into a single meta-document and then summarize it using an SDS model. |
| Outcome: | The proposed approach outperforms state-of-the-art methods with more complex architectures. |
Is Everything in Order? A Simple Way to Order Sentences (2021.emnlp-main)
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| Challenge: | Existing work on sentence ordering has focused on exploiting different categories of features like coreference clues. |
| Approach: | They propose a sentence ordering task as a conditional text-to-marker generation problem that leverages a pre-trained Transformer-based model to identify a coherent order for a given set of shuffled sentences. |
| Outcome: | The proposed model performs well across 7 datasets in Perfect Match Ratio and Kendall’s tau. |
Aspect-aware Unsupervised Extractive Opinion Summarization (2023.findings-acl)
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| Challenge: | Extractive opinion summarization extracts sentences from reviews to represent the prevalent opinions about a product or service. |
| Approach: | They propose a method for unsupervised extractive opinion summarization that automatically identifies the aspects described in review sentences and extracts sentences based on their aspects. |
| Outcome: | The proposed method improves aspect coverage and performs well on multiple opinion summarization datasets. |
Affective and Dynamic Beam Search for Story Generation (2023.findings-emnlp)
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| Challenge: | AffGen introduces ‘intriguing twists’ in narratives by employing two novel techniques—Dynamic Beam Sizing and Affective Reranking. |
| Approach: | They propose to use dynamic beam sizing and affective reranking to generate interesting stories using two novel techniques. |
| Outcome: | The proposed method outperforms baseline models in generating affectively charged and interesting narratives. |
Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation (2020.acl-main)
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| Challenge: | Current sequence-to-sequence models require serialized input, resulting in loss of structural information. |
| Approach: | They propose a dual encoding model that incorporates the graph structure and caters to the linear structure of the output text. |
| Outcome: | Empirical results show that dual encoding can improve the quality of natural language descriptions. |
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding (2021.findings-emnlp)
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| Challenge: | Existing studies on character-centric understanding of narratives focus on understanding the characters in the narrative, but these studies are limited to understanding only certain aspects of characters. |
| Approach: | They propose a dataset of literary pieces and their summaries paired with descriptions of characters that appear in them that are used to facilitate character-centric narrative understanding. |
| Outcome: | The proposed dataset includes literary pieces and their summaries paired with descriptions of characters that appear in them. |
SPE: Symmetrical Prompt Enhancement for Fact Probing (2022.emnlp-main)
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| Challenge: | Recent work probes PLMs for the extent of factual knowledge through prompts . however, these methods do not consider symmetry of the task: object and subject prediction. |
| Approach: | They propose a continuous prompt-based method that leverages symmetry of the task by constructing symmetrical prompts for subject and object prediction. |
| Outcome: | The proposed method improves on a popular factual probing dataset on lAMA. |
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach (2022.findings-naacl)
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| Challenge: | Existing approaches to generative commonsense reasoning hypothesize that pre-trained models lack sufficient parametric knowledge for this task. |
| Approach: | They propose to use order-agnostic input to elaborately manipulate the order of the given concepts before generation to evaluate their commonsense knowledge. |
| Outcome: | The proposed approach outperforms more sophisticated models with a lot of external data and resources in the task of generating a logical sentence from a set of concepts. |