Papers by Wee Chung Gan
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations (D19-1)
Copied to clipboard
| Challenge: | Contextualized word representations are effective in downstream tasks such as question answering, named entity recognition, and sentiment analysis. |
| Approach: | They propose to integrate pre-trained contextualized word representations into a neural network that captures the whole sentence and the word representation in the sentence. |
| Outcome: | The proposed approach outperforms the state-of-the-art approach that makes use of non-contextualized word embeddings on multiple benchmark WSD datasets. |
Improving the Robustness of Question Answering Systems to Question Paraphrasing (P19-1)
Copied to clipboard
| Challenge: | Despite advancement of question answering systems, generalizability of QA models is a topic of concern. |
| Approach: | They propose to use a neural paraphrasing model to generate multiple paraphrased questions for a given source question and a set of paraphrase suggestions to re-train the models. |
| Outcome: | The proposed approach requires no human intervention to re-train the models for improved robustness to question paraphrasing. |