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Found 45 papers, 23 papers with code
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc.
Coverage-based Fairness in Multi-document Summarization
In this work, we propose a new summary-level fairness measure, Equal Coverage, which is based on coverage of documents with different social attribute values and considers the redundancy within documents.
SocialGaze: Improving the Integration of Human Social Norms in Large Language Models
We find that LLMs' understanding of social acceptance is often misaligned with human consensus.
Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning
Parameter-efficient fine-tuning (PEFT) approaches have emerged as a viable alternative by allowing us to fine-tune models by updating only a small number of parameters.
Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning
Exact unlearning approaches use a machine learning model in which individual components are trained on disjoint subsets of the data.
Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers
We present a new class of fast polylog-linear algorithms based on the theory of structured matrices (in particular low displacement rank) for integrating tensor fields defined on weighted trees.
Exploring Safety-Utility Trade-Offs in Personalized Language Models
We measure utility by evaluating the LLM's performance on various tasks, including general knowledge, mathematical abilities, programming, and reasoning skills.
Returning to the Start: Generating Narratives with Related Endpoints
Human writers often bookend their writing with ending sentences that relate back to the beginning sentences in order to compose a satisfying narrative that "closes the loop."
Rationale-based Opinion Summarization
Opinion summarization aims to generate concise summaries that present popular opinions of a large group of reviews.
Incremental Extractive Opinion Summarization Using Cover Trees
In this work, we study the task of extractive opinion summarization in an incremental setting, where the underlying review set evolves over time.
Robust Concept Erasure via Kernelized Rate-Distortion Maximization
Distributed representations provide a vector space that captures meaningful relationships between data instances.
Affective and Dynamic Beam Search for Story Generation
Storytelling's captivating potential makes it a fascinating research area, with implications for entertainment, education, therapy, and cognitive studies.
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests
In the online setting, where the algorithm has access to a single instance at a time, estimating the group fairness objective requires additional storage and significantly more computation (e. g., forward/backward passes) than the task-specific objective at every time step.
Efficient Graph Field Integrators Meet Point Clouds
We present two new classes of algorithms for efficient field integration on graphs encoding point clouds.
Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation
We propose a new grounded keys-to-text generation task: the task is to generate a factual description about an entity given a set of guiding keys, and grounding passages.
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization
To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset.
SPE: Symmetrical Prompt Enhancement for Fact Probing
In this work, we propose Symmetrical Prompt Enhancement (SPE), a continuous prompt-based method for factual probing in PLMs that leverages the symmetry of the task by constructing symmetrical prompts for subject and object prediction.
Towards Inter-character Relationship-driven Story Generation
In this paper, we introduce the task of modeling interpersonal relationships for story generation.
Unsupervised Opinion Summarization Using Approximate Geodesics
We then use these representations to quantify the relevance of review sentences using a novel approximate geodesic distance based scoring mechanism.
Sustaining Fairness via Incremental Learning
In this work, we propose to address this issue by introducing the problem of learning fair representations in an incremental learning setting.
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach
In particular, we hypothesize that the order of the input concepts can affect the PTM's ability to utilize its commonsense knowledge.
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document.
Unsupervised Extractive Opinion Summarization Using Sparse Coding
A semantic unit is supposed to capture an abstract semantic concept.
Learning Fair Representations via Rate-Distortion Maximization
Text representations learned by machine learning models often encode undesirable demographic information of the user.
Does Commonsense help in detecting Sarcasm?
For this, we incorporate commonsense knowledge into the prediction process using a graph convolution network with pre-trained language model embeddings as input.
Adversarial Scrubbing of Demographic Information for Text Classification
Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task.
Uncovering Implicit Gender Bias in Narratives through Commonsense Inference
Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation.
"Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding
When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc.
How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation?
Many approaches to this problem use Reinforcement Learning (RL), which maximizes a single manually defined reward, such as BLEU.
Is Everything in Order? A Simple Way to Order Sentences
We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets.
Cue Me In: Content-Inducing Approaches to Interactive Story Generation
Previous approaches in this domain have focused largely on one-shot generation, where a language model outputs a complete story based on limited initial input from a user.
Modeling Protagonist Emotions for Emotion-Aware Storytelling
Emotions and their evolution play a central role in creating a captivating story.
Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation
Generating sequential natural language descriptions from graph-structured data (e. g., knowledge graph) is challenging, partly because of the structural differences between the input graph and the output text.
Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts
Accurately diagnosing depression is difficult{--} requiring time-intensive interviews, assessments, and analysis.
Weakly-Supervised Opinion Summarization by Leveraging External Information
Opinion summarization from online product reviews is a challenging task, which involves identifying opinions related to various aspects of the product being reviewed.
Named Entity Recognition with Partially Annotated Training Data
Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated.
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences.
Where Have I Heard This Story Before? Identifying Narrative Similarity in Movie Remakes
People can identify correspondences between narratives in everyday life.
Story Comprehension for Predicting What Happens Next
Automatic story comprehension is a fundamental challenge in Natural Language Understanding, and can enable computers to learn about social norms, human behavior and commonsense.
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Question Answering
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A Joint Model for Semantic Sequences: Frames, Entities, Sentiments
Understanding stories {--} sequences of events {--} is a crucial yet challenging natural language understanding task.
Inferring Interpersonal Relations in Narrative Summaries
In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries.
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding.
Modeling Dynamic Relationships Between Characters in Literary Novels
Studying characters plays a vital role in computationally representing and interpreting narratives.
