Papers by Angelica Chen
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey (2023.acl-long)
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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. |
SQuALITY: Building a Long-Document Summarization Dataset the Hard Way (2022.emnlp-main)
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| Challenge: | Existing summarization datasets often have issues that seriously limit their usability. |
| Approach: | They propose a faster but more straightforward approach to developing summarization benchmark data . they use a protocol that hires highly-qualified contractors to read stories and write original summaries from scratch . |
| Outcome: | The proposed protocol is faster but more straightforward than scraping summaries from everyday text. |
Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming Disfluency Detection (2022.naacl-main)
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| Challenge: | a recent study shows that current NLP models operate non-incrementally, causing unacceptable delays for the user. |
| Approach: | They propose a streaming BERT-based sequence tagging model that detects disfluencies in real-time . they train the model to decide whether to immediately output a prediction or wait for further context . |
| Outcome: | The proposed model produces accurate predictions sooner than baselines, with lower flicker . disfluencies hurt readability of ASR transcripts, erode model performance on downstream tasks . |
QuALITY: Question Answering with Long Input Texts, Yes! (2022.naacl-main)
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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)
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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. |
Generating Logical Forms from Graph Representations of Text and Entities (P19-1)
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| Challenge: | Recent approaches to semantic parsing have cast it as a sequence-to-sequence task, with strong results. |
| Approach: | They propose a Graph Neural Network architecture to incorporate information about relevant entities and their relations during parsing. |
| Outcome: | The proposed approach outperforms the state-of-the-art in several tasks without pre-training and outperformed existing approaches when combined with BERT pre-trainment. |