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Found 25 papers, 8 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.
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.
Where Have I Heard This Story Before? Identifying Narrative Similarity in Movie Remakes
People can identify correspondences between narratives in everyday life.
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.
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.
Ranked #6 on
Question Answering
on Story Cloze Test
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.
Modeling Dynamic Relationships Between Characters in Literary Novels
Studying characters plays a vital role in computationally representing and interpreting narratives.
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.
