Papers by Lim Yao Chong
Towards Debiasing Sentence Representations (2020.acl-main)
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Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Recent work has shown word-level embeddings reflect and propagate social biases present in training corpora. |
| Approach: | They propose a method to debias word embeddings to reduce biases at sentence level . they hope their work will inspire future research on characterizing and removing biase . |
| Outcome: | The proposed method reduces biases and preserves performance on downstream tasks such as sentiment analysis and natural language understanding. |
Strong and Simple Baselines for Multimodal Utterance Embeddings (N19-1)
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| Challenge: | Human language is a rich multimodal signal consisting of spoken words, facial expressions, body gestures, and vocal intonations. |
| Approach: | They propose two simple but strong baselines to learn embeddings of multimodal utterances by factorizing the utterant into unimodal factors. |
| Outcome: | The proposed models show that they can be derived in closed form while maintaining simplicity and efficiency during learning and inference. |
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (N19-1)
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| Challenge: | Existing methods to debias word embeddings in binary settings such as gender and religion are limited to binary labels, whereas word2vec embedders can be used to propagate biases. |
| Approach: | They propose a method to debias word embeddings in multiclass settings such as gender and religion, extending the work of Bolukbasi et al. (2016). |
| Outcome: | The proposed method maintains the efficacy in standard NLP tasks while maintaining the utility of embeddings. |