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The Risk of Racial Bias in Hate Speech Detection
This work proposes *dialect* and *race priming* as ways to reduce the racial bias in annotation, showing that when annotators are made explicitly aware of an AAE tweet’s dialect they are significantly less likely to label the tweet as offensive.
Social Bias Frames: Reasoning about Social and Power Implications of Language
- Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, Yejin Choi
- Computer ScienceACL
- 10 November 2019
It is found that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias, they are not effective at spelling out more detailed explanations in terms of Social Bias Frames.
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
- Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, Hannaneh Hajishirzi
- Computer ScienceNAACL
- 30 May 2019
A large-scale dataset of math word problems and an interpretable neural math problem solver by learning to map problems to their operation programs and a new representation language to model operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models.
Paragraph-Level Commonsense Transformers with Recurrent Memory
- Saadia Gabriel, Chandra Bhagavatula, Vered Shwartz, Ronan Le Bras, Maxwell Forbes, Yejin Choi
- Computer ScienceAAAI
- 4 October 2020
PARA-COMeT, a discourse-aware model that incorporates paragraph-level information to generate coherent commonsense inferences from narratives, outperforms the sentence-level baselines, particularly in generating inferences that are both coherent and novel.
GO FIGURE: A Meta Evaluation of Factuality in Summarization
- Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, Yejin Choi, Jianfeng Gao
- Computer ScienceFINDINGS
- 24 October 2020
This paper introduces a meta-evaluation framework for evaluating factual consistency metrics and experiments with nine recent factuality metrics using synthetic and human-labeled factuality data from short news, long news and dialogue summarization domains.
Cooperative Generator-Discriminator Networks for Abstractive Summarization with Narrative Flow
- Saadia Gabriel, Antoine Bosselut, Ari Holtzman, Kyle Lo, A. Çelikyilmaz, Yejin Choi
- Computer ScienceArXiv
- 2 July 2019
To promote research toward abstractive summarization with narrative flow, a new dataset is introduced, Scientific Abstract SummarieS (SASS), where the abstracts are used as proxy gold summaries for scientific articles and Co-opNet is proposed, a novel transformer-based framework where the generator works with the discourse discriminator to compose a long-form summary.
EARLY FUSION for Goal Directed Robotic Vision
- Aaron Walsman, Yonatan Bisk, +4 authors D. Fox
- Computer ScienceIEEE/RSJ International Conference on Intelligent…
- 21 November 2018
This work introduces EARLYFUSION vision models that condition on a goal to build custom representations for downstream tasks, and shows that these goal specific representations can be learned more quickly, are substantially more parameter efficient, and more robust than existing attention mechanisms in the domain.
Discourse Understanding and Factual Consistency in Abstractive Summarization
A general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary is introduced and empirical results demonstrate that Co-opNet learns to summarize with considerably improved global coherence compared to competitive baselines.
Detecting and Tracking Communal Bird Roosts in Weather Radar Data
A machine learning system to detect and track roost signatures in weather radar data that detects previously unknown roosting locations and provides comprehensive spatio-temporal data about roosts across the US.