30 citations · 56 across the 19 of their papers we have counts for
23 papers
Efficient Graph Field Integrators Meet Point Clouds
Krzysztof Choromanski, Arijit Sehanobish, Han Lin +13
We present two new classes of algorithms for efficient field integration on graphs encoding point clouds. The first class, SeparatorFactorization(SF), leverages the bounded genus o…
Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation
Faeze Brahman, Baolin Peng, Michel Galley +4
Large pre-trained language models have recently enabled open-ended generation frameworks (e.g., prompt-to-text NLG) to tackle a variety of tasks going beyond the traditional data-t…
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization
Chao Zhao, Faeze Brahman, Kaiqiang Song +3
Narrative summarization aims to produce a distilled version of a narrative to describe its most salient events and characters. Summarizing a narrative is challenging as it requires…
SPE: Symmetrical Prompt Enhancement for Fact Probing
Yiyuan Li, Tong Che, Yezhen Wang +3
Pretrained language models (PLMs) have been shown to accumulate factual knowledge during pretrainingng (Petroni et al., 2019). Recent works probe PLMs for the extent of this knowle…
Towards Inter-character Relationship-driven Story Generation
Anvesh Rao Vijjini, Faeze Brahman, Snigdha Chaturvedi
In this paper, we introduce the task of modeling interpersonal relationships for story generation. For addressing this task, we propose Relationships as Latent Variables for Story…
Unsupervised Opinion Summarization Using Approximate Geodesics
Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey +2
Opinion summarization is the task of creating summaries capturing popular opinions from user reviews. In this paper, we introduce Geodesic Summarizer (GeoSumm), a novel system to p…
Sustaining Fairness via Incremental Learning
Somnath Basu Roy Chowdhury, Snigdha Chaturvedi
Machine learning systems are often deployed for making critical decisions like credit lending, hiring, etc. While making decisions, such systems often encode the user's demographic…
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach
Chao Zhao, Faeze Brahman, Tenghao Huang +1
Pre-trained models (PTMs) have lead to great improvements in natural language generation (NLG). However, it is still unclear how much commonsense knowledge they possess. With the g…
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury +3
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-do…
Unsupervised Extractive Opinion Summarization Using Sparse Coding
Somnath Basu Roy Chowdhury, Chao Zhao, Snigdha Chaturvedi
Opinion summarization is the task of automatically generating summaries that encapsulate information from multiple user reviews. We present Semantic Autoencoder (SemAE) to perform…
Learning Fair Representations via Rate-Distortion Maximization
Somnath Basu Roy Chowdhury, Snigdha Chaturvedi
Text representations learned by machine learning models often encode undesirable demographic information of the user. Predictive models based on these representations can rely on s…
Adversarial Scrubbing of Demographic Information for Text Classification
Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li +3
Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated targ…
Does Commonsense help in detecting Sarcasm?
Somnath Basu Roy Chowdhury, Snigdha Chaturvedi
Sarcasm detection is important for several NLP tasks such as sentiment identification in product reviews, user feedback, and online forums. It is a challenging task requiring a dee…
Uncovering Implicit Gender Bias in Narratives through Commonsense Inference
Tenghao Huang, Faeze Brahman, Vered Shwartz +1
Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation. We study gender biases associated with…
"Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding
Faeze Brahman, Meng Huang, Oyvind Tafjord +3
When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc. While humans can readily draw upo…
Is Everything in Order? A Simple Way to Order Sentences
Somnath Basu Roy Chowdhury, Faeze Brahman, Snigdha Chaturvedi
The task of organizing a shuffled set of sentences into a coherent text has been used to evaluate a machine's understanding of causal and temporal relations. We formulate the sente…
Cue Me In: Content-Inducing Approaches to Interactive Story Generation
Faeze Brahman, Alexandru Petrusca, Snigdha Chaturvedi
Automatically generating stories is a challenging problem that requires producing causally related and logical sequences of events about a topic. Previous approaches in this domain…
Modeling Protagonist Emotions for Emotion-Aware Storytelling
Faeze Brahman, Snigdha Chaturvedi
Emotions and their evolution play a central role in creating a captivating story. In this paper, we present the first study on modeling the emotional trajectory of the protagonist…
Weakly-Supervised Opinion Summarization by Leveraging External Information
Chao Zhao, Snigdha Chaturvedi
Opinion summarization from online product reviews is a challenging task, which involves identifying opinions related to various aspects of the product being reviewed. While previou…
Named Entity Recognition with Partially Annotated Training Data
Stephen Mayhew, Snigdha Chaturvedi, Chen-Tse Tsai +1
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. We st…
Discovering Archetypes to Interpret Evolution of Individual Behavior
Kanika Narang, Austin Chung, Hari Sundaram +1
In this paper, we aim to discover archetypical patterns of individual evolution in large social networks. In our work, an archetype comprises of of di…
Inferring Interpersonal Relations in Narrative Summaries
Shashank Srivastava, Snigdha Chaturvedi, Tom Mitchell
Characterizing relationships between people is fundamental for the understanding of narratives. In this work, we address the problem of inferring the polarity of relationships betw…
Modeling Dynamic Relationships Between Characters in Literary Novels
Snigdha Chaturvedi, Shashank Srivastava, Hal Daume +1
Studying characters plays a vital role in computationally representing and interpreting narratives. Unlike previous work, which has focused on inferring character roles, we focus o…