Papers by Shikha Bordia
Identifying and Reducing Gender Bias in Word-Level Language Models (N19-3)
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| Challenge: | Existing discriminatory biases in training data can be amplified by models . text corpora exhibit socially problematic biase . |
| Approach: | They propose a metric to measure gender bias and a regularization loss term to minimize embeddings onto an embeddable subspace that encodes gender. |
| Outcome: | The proposed method reduces gender bias up to an optimal weight assigned to the loss term, and the model becomes unstable as the perplexity increases. |
On Measuring Social Biases in Sentence Encoders (N19-1)
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| Challenge: | Word embeddings such as word2vec and GloVe exhibit human-like implicit biases based on gender, race, and other social constructs. |
| Approach: | They propose a simple generaliza test to measure bias in word embeddings by comparing two sets of target-concept words to two sets . |
| Outcome: | The proposed test shows that word2vec and word2Ve exhibit human-like implicit biases based on gender, race, and other social constructs. |
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (D19-1)
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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. |
HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification (2020.findings-emnlp)
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| Challenge: | a dataset for many-hop evidence extraction and fact verification challenges models to reason with information from multiple Wikipedia articles. |
| Approach: | They propose a dataset for many-hop evidence extraction and fact verification . they challenge models to extract facts from Wikipedia articles relevant to a claim . |
| Outcome: | The proposed dataset shows that state-of-the-art models degrade as the number of reasoning hops increases. |