Search Results for author: Snigdha Chaturvedi

Found 25 papers, 8 papers with code

“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding

no code implementations Findings (EMNLP) 2021 Faeze Brahman, Meng Huang, Oyvind Tafjord, Chao Zhao, Mrinmaya Sachan, Snigdha Chaturvedi

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

1 code implementation Findings (ACL) 2022 Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, Snigdha Chaturvedi

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.

Document Summarization News Summarization

Learning Fair Representations via Rate-Distortion Maximization

no code implementations31 Jan 2022 Somnath Basu Roy Chowdhury, Snigdha Chaturvedi

Text representations learned by machine learning models often encode undesirable demographic information of the user.

Fairness

Does Commonsense help in detecting Sarcasm?

1 code implementation EMNLP (insights) 2021 Somnath Basu Roy Chowdhury, Snigdha Chaturvedi

For this, we incorporate commonsense knowledge into the prediction process using a graph convolution network with pre-trained language model embeddings as input.

Language Modelling Sarcasm Detection

Adversarial Scrubbing of Demographic Information for Text Classification

1 code implementation EMNLP 2021 Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li, Junier B. Oliva, Shashank Srivastava, Snigdha Chaturvedi

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.

Classification Text Classification

Uncovering Implicit Gender Bias in Narratives through Commonsense Inference

1 code implementation Findings (EMNLP) 2021 Tenghao Huang, Faeze Brahman, Vered Shwartz, Snigdha Chaturvedi

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

no code implementations12 Sep 2021 Faeze Brahman, Meng Huang, Oyvind Tafjord, Chao Zhao, Mrinmaya Sachan, Snigdha Chaturvedi

When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc.

Cue Me In: Content-Inducing Approaches to Interactive Story Generation

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Faeze Brahman, Alexandru Petrusca, Snigdha Chaturvedi

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.

Language Modelling Story Generation

Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation

no code implementations ACL 2020 Chao Zhao, Marilyn Walker, Snigdha Chaturvedi

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.

Data-to-Text Generation

Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts

no code implementations ACL 2020 Alex Rinaldi, Jean Fox Tree, Snigdha Chaturvedi

Accurately diagnosing depression is difficult{--} requiring time-intensive interviews, assessments, and analysis.

Weakly-Supervised Opinion Summarization by Leveraging External Information

no code implementations22 Nov 2019 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.

Named Entity Recognition with Partially Annotated Training Data

no code implementations CONLL 2019 Stephen Mayhew, Snigdha Chaturvedi, Chen-Tse Tsai, Dan Roth

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.

Named Entity Recognition NER

Story Comprehension for Predicting What Happens Next

no code implementations EMNLP 2017 Snigdha Chaturvedi, Haoruo Peng, Dan Roth

Automatic story comprehension is a fundamental challenge in Natural Language Understanding, and can enable computers to learn about social norms, human behavior and commonsense.

Common Sense Reasoning Natural Language Understanding +3

A Joint Model for Semantic Sequences: Frames, Entities, Sentiments

no code implementations CONLL 2017 Haoruo Peng, Snigdha Chaturvedi, Dan Roth

Understanding stories {--} sequences of events {--} is a crucial yet challenging natural language understanding task.

Cloze Test Discourse Parsing +2

Inferring Interpersonal Relations in Narrative Summaries

no code implementations1 Dec 2015 Shashank Srivastava, Snigdha Chaturvedi, Tom Mitchell

In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries.

Structured Prediction

Modeling Dynamic Relationships Between Characters in Literary Novels

no code implementations30 Nov 2015 Snigdha Chaturvedi, Shashank Srivastava, Hal Daume III, Chris Dyer

Studying characters plays a vital role in computationally representing and interpreting narratives.

Structured Prediction

Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text

no code implementations30 Nov 2015 Snigdha Chaturvedi, Dan Goldwasser, Hal Daume III

The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding.

Natural Language Understanding

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