68 citations · 158 across the 9 of their papers we have counts for
9 papers
Investigating Agency of LLMs in Human-AI Collaboration Tasks
Ashish Sharma, Sudha Rao, Chris Brockett +3
Agency, the capacity to proactively shape events, is central to how humans interact and collaborate. While LLMs are being developed to simulate human behavior and serve as human-li…
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…
Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages
Paul Soulos, Sudha Rao, Caitlin Smith +9
Machine translation has seen rapid progress with the advent of Transformer-based models. These models have no explicit linguistic structure built into them, yet they may still impl…
Answer-based Adversarial Training for Generating Clarification Questions
Sudha Rao, Hal Daumé
We present an approach for generating clarification questions with the goal of eliciting new information that would make the given textual context more complete. We propose that mo…
Multi-Task Neural Models for Translating Between Styles Within and Across Languages
Xing Niu, Sudha Rao, Marine Carpuat
Generating natural language requires conveying content in an appropriate style. We explore two related tasks on generating text of varying formality: monolingual formality transfer…
Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information
Sudha Rao, Hal Daumé
Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this work, we build a neural network model for th…
Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer
Sudha Rao, Joel Tetreault
Style transfer is the task of automatically transforming a piece of text in one particular style into another. A major barrier to progress in this field has been a lack of training…
Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task
Allyson Ettinger, Sudha Rao, Hal Daumé +1
This paper presents a summary of the first Workshop on Building Linguistically Generalizable Natural Language Processing Systems, and the associated Build It Break It, The Language…
Parser for Abstract Meaning Representation using Learning to Search
Sudha Rao, Yogarshi Vyas, Hal Daume +1
We develop a novel technique to parse English sentences into Abstract Meaning Representation (AMR) using SEARN, a Learning to Search approach, by modeling the concept and the relat…