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.
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.
1 code implementation • ACL 2022 • Somnath Basu Roy Chowdhury, Chao Zhao, Snigdha Chaturvedi
A semantic unit is supposed to capture an abstract semantic concept.
no code implementations • 31 Jan 2022 • Somnath Basu Roy Chowdhury, Snigdha Chaturvedi
Text representations learned by machine learning models often encode undesirable demographic information of the user.
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.
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.
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.
no code implementations • 12 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.
1 code implementation • ACL 2021 • Sayan Ghosh, Zheng Qi, Snigdha Chaturvedi, Shashank Srivastava
Many approaches to this problem use Reinforcement Learning (RL), which maximizes a single manually defined reward, such as BLEU.
1 code implementation • EMNLP 2021 • Somnath Basu Roy Chowdhury, Faeze Brahman, Snigdha Chaturvedi
We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets.
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.
1 code implementation • EMNLP 2020 • Faeze Brahman, Snigdha Chaturvedi
Emotions and their evolution play a central role in creating a captivating story.
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.
no code implementations • ACL 2020 • Alex Rinaldi, Jean Fox Tree, Snigdha Chaturvedi
Accurately diagnosing depression is difficult{--} requiring time-intensive interviews, assessments, and analysis.
no code implementations • 22 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.
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.
no code implementations • NAACL 2018 • Snigdha Chaturvedi, Shashank Srivastava, Dan Roth
People can identify correspondences between narratives in everyday life.
no code implementations • NAACL 2018 • Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, Dan Roth
We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences.
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.
Ranked #6 on
Question Answering
on Story Cloze Test
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.
no code implementations • 1 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.
no code implementations • 30 Nov 2015 • Snigdha Chaturvedi, Shashank Srivastava, Hal Daume III, Chris Dyer
Studying characters plays a vital role in computationally representing and interpreting narratives.
no code implementations • 30 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.