Papers by Lisa Wang
In Plain Sight: Media Bias Through the Lens of Factual Reporting (D19-1)
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| Challenge: | lexical bias stems from content realization, or how things are said, but other forms of bias stem from content selection and organization. |
| Approach: | They use a dataset to analyze news articles annotated with 1,727 bias spans to investigate informational bias. |
| Outcome: | The proposed model shows that informational bias appears more frequently than lexical bias. |
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets (2022.tacl-1)
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Julia Kreutzer, Isaac Caswell, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro Ortiz Suarez, Iroro Orife, Kelechi Ogueji, Andre Niyongabo Rubungo, Toan Q. Nguyen, Mathias Müller, André Müller, Shamsuddeen Hassan Muhammad, Nanda Muhammad, Ayanda Mnyakeni, Jamshidbek Mirzakhalov, Tapiwanashe Matangira, Colin Leong, Nze Lawson, Sneha Kudugunta, Yacine Jernite, Mathias Jenny, Orhan Firat, Bonaventure F. P. Dossou, Sakhile Dlamini, Nisansa de Silva, Sakine Çabuk Ballı, Stella Biderman, Alessia Battisti, Ahmed Baruwa, Ankur Bapna, Pallavi Baljekar, Israel Abebe Azime, Ayodele Awokoya, Duygu Ataman, Orevaoghene Ahia, Oghenefego Ahia, Sweta Agrawal, Mofetoluwa Adeyemi
| Challenge: | Lower-resource corpora have systematic issues, including mislabeled or nonstandard/ambiguous language codes. |
| Approach: | They manually audit the quality of 205 language-specific corpora released with five major public datasets. |
| Outcome: | The results show that lower-resource corpora have systematic issues even for non-proficient speakers. |
Commonsense for Generative Multi-Hop Question Answering Tasks (D18-1)
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| Challenge: | Reading comprehension QA tasks have seen a recent surge in popularity, yet most work has focused on fact-finding extractive QA. |
| Approach: | They propose a multi-hop generative task that uses a pointer-generator decoder to synthesize disjoint pieces of information within the context to generate an answer. |
| Outcome: | The proposed model performs better than previous generative models and is competitive with current state-of-the-art span prediction models. |