Papers by Alicia Parrish
GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives (2024.naacl-long)
Copied to clipboard
Vinodkumar Prabhakaran, Christopher Homan, Lora Aroyo, Aida Mostafazadeh Davani, Alicia Parrish, Alex Taylor, Mark Diaz, Ding Wang, Gregory Serapio-García
| Challenge: | Recent work shows that ignoring rater subjectivity is problematic within specific tasks and for specific subgroups. |
| Approach: | They propose a disagreement analysis framework to measure group association in perspectives among different rater subgroups. |
| Outcome: | The proposed framework reveals specific rater groups that have significantly different perspectives than others on certain tasks and helps identify demographic axes that are crucial to consider in specific task contexts. |
Does Putting a Linguist in the Loop Improve NLU Data Collection? (2021.findings-emnlp)
Copied to clipboard
Alicia Parrish, William Huang, Omar Agha, Soo-Hwan Lee, Nikita Nangia, Alexia Warstadt, Karmanya Aggarwal, Emily Allaway, Tal Linzen, Samuel R. Bowman
| Challenge: | Many datasets for training and evaluating natural language understanding (NLU) models contain systematic artifacts that are identified only after data collection is complete. |
| Approach: | They propose to have linguists identify artifacts and gaps in the data and communicate with non-expert crowdworkers to adjust task instructions and incentives. |
| Outcome: | The proposed protocol does not increase accuracy on out-of-domain test sets, and adds a chatroom does not. |
BLiMP: The Benchmark of Linguistic Minimal Pairs for English (2020.tacl-1)
Copied to clipboard
Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, Samuel R. Bowman
| Challenge: | Recent studies have examined how linguistic knowledge of language models (LMs) varies across English phenomena. |
| Approach: | They propose a benchmark to evaluate linguistic knowledge of language models on major grammatical phenomena in English. |
| Outcome: | The proposed benchmark evaluates the linguistic knowledge of language models on major grammatical phenomena in English. |
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey (2023.acl-long)
Copied to clipboard
Julian Michael, Ari Holtzman, Alicia Parrish, Aaron Mueller, Alex Wang, Angelica Chen, Divyam Madaan, Nikita Nangia, Richard Yuanzhe Pang, Jason Phang, Samuel R. Bowman
| Challenge: | Getting sociological beliefs wrong can slow research and lead to wasted effort, missed opportunities, and needless fights. |
| Approach: | They present the results of the NLP Community Metasurvey, run from May to June 2022. |
| Outcome: | The NLP community metasurvey elicited opinions on controversial issues from May to June 2022. |
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (D19-1)
Copied to clipboard
Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretic, Samuel R. Bowman
| Challenge: | Recent work evaluating sentence representation models' knowledge of grammar has been slower to emerge. |
| Approach: | They propose five experimental methods inspired by prior work evaluating pretrained sentence representation models to examine their grammatical knowledge. |
| Outcome: | The proposed methods show that the model has significant knowledge of the licensing environment but its success varies widely across different methods. |
QuALITY: Question Answering with Long Input Texts, Yes! (2022.naacl-main)
Copied to clipboard
Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, Samuel Bowman
| Challenge: | Existing models for natural language understanding are limited to processing only a few hundred words at a time. |
| Approach: | They propose a dataset with context passages in English that have an average length of 5,000 tokens. |
| Outcome: | a new dataset with long-text comprehension questions is used to test models on long-document comprehension . the questions are validated by contributors who have read the entire passage, not just excerpts . only half of the questions can be answered by annotators working under tight time constraints . |
BBQ: A hand-built bias benchmark for question answering (2022.findings-acl)
Copied to clipboard
Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, Samuel Bowman
| Challenge: | NLP models learn social biases, but little work has been done on how these biase manifest in outputs for applied tasks like question answering (QA). |
| Approach: | They propose a dataset that highlights attested social biases against people belonging to protected classes along nine social dimensions relevant for U.S. English-speaking contexts. |
| Outcome: | The proposed dataset highlights attested social biases against people belonging to protected classes along nine social dimensions relevant for U.S. English-speaking contexts. |