Search Results for author:
Found 13 papers, 7 papers with code
Where Are We in Discourse Relation Recognition?
Discourse parsers recognize the intentional and inferential relationships that organize extended texts.
The Role of Context and Uncertainty in Shallow Discourse Parsing
We find that including annotator accuracy and confidence improves model accuracy, and incorporating confidence in the model’s temperature function can lead to models with significantly better-calibrated confidence measures.
Political Ideology and Polarization: A Multi-dimensional Approach
Analyzing ideology and polarization is of critical importance in advancing our grasp of modern politics.
Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI
Evaluated in home improvement and cooking domains with real-world users, our method improves recall and F1 of correction by 34% and 16%, respectively, while maintaining precision and false positive rate.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Generating Signed Language Instructions in Large-Scale Dialogue Systems
We introduce a goal-oriented conversational AI system enhanced with American Sign Language (ASL) instructions, presenting the first implementation of such a system on a worldwide multimodal conversational AI platform.
Studying and Mitigating Biases in Sign Language Understanding Models
Ensuring that the benefits of sign language technologies are distributed equitably among all community members is crucial.
Dialogue with Robots: Proposals for Broadening Participation and Research in the SLIVAR Community
The ability to interact with machines using natural human language is becoming not just commonplace, but expected.
Multilingual Content Moderation: A Case Study on Reddit
Content moderation is the process of flagging content based on pre-defined platform rules.
APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations
Then, we introduce the first discourse-aware style-transfer models that can effectively reduce offensiveness in Reddit text while preserving the meaning of the original text.
How people talk about each other: Modeling Generalized Intergroup Bias and Emotion
Current studies of bias in NLP rely mainly on identifying (unwanted or negative) bias towards a specific demographic group.
PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners
Multiclass neural networks are a common tool in modern unsupervised domain adaptation, yet an appropriate theoretical description for their non-uniform sample complexity is lacking in the adaptation literature.
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error
Our results not only motivate our proposal and help us to understand its limitations, but also provide insight on the properties of discourse models and datasets which improve performance in domain adaptation.
Political Ideology and Polarization of Policy Positions: A Multi-dimensional Approach
Analyzing ideology and polarization is of critical importance in advancing our grasp of modern politics.
