Search Results for author: Parker Glenn

Found 4 papers, 2 papers with code

The Viability of Best-worst Scaling and Categorical Data Label Annotation Tasks in Detecting Implicit Bias

no code implementations NLPerspectives (LREC) 2022 Parker Glenn, Cassandra L. Jacobs, Marvin Thielk, Yi Chu

We identify several shortcomings of BWS relative to traditional categorical annotation: (1) When compared to categorical annotation, we estimate BWS takes approximately 4. 5x longer to complete; (2) BWS does not scale well to large annotation tasks with sparse target phenomena; (3) The high correlation between BWS and the traditional task shows that the benefits of BWS can be recovered from a simple categorically annotated, non-aggregated dataset.

BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra

1 code implementation27 Feb 2024 Parker Glenn, Parag Pravin Dakle, Liang Wang, Preethi Raghavan

Many existing end-to-end systems for hybrid question answering tasks can often be boiled down to a "prompt-and-pray" paradigm, where the user has limited control and insight into the intermediate reasoning steps used to achieve the final result.

Question Answering

Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding

1 code implementation31 May 2023 Parker Glenn, Parag Pravin Dakle, Preethi Raghavan

In addressing the task of converting natural language to SQL queries, there are several semantic and syntactic challenges.

Semantic Parsing Text-To-SQL

Designing Multimodal Datasets for NLP Challenges

no code implementations12 May 2021 James Pustejovsky, Eben Holderness, Jingxuan Tu, Parker Glenn, Kyeongmin Rim, Kelley Lynch, Richard Brutti

In this paper, we argue that the design and development of multimodal datasets for natural language processing (NLP) challenges should be enhanced in two significant respects: to more broadly represent commonsense semantic inferences; and to better reflect the dynamics of actions and events, through a substantive alignment of textual and visual information.

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