Papers by Eric Lind
Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation (2022.aacl-main)
Copied to clipboard
| Challenge: | Open-Domain Generative Question Answering has achieved impressive performance in English . combining document-level retrieval with answer generation can generate complete sentences . |
| Approach: | They propose an open-domain approach that combines document retrieval with answer generation to generate complete sentences in English . they propose a cross-lingual generative model that exploits passages written in multiple languages . |
| Outcome: | The proposed model outperforms answer sentence selection baselines for all 5 languages and monolingual pipelines for three out of five languages. |
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Large transformer models are expensive and slow to use in many applications. |
| Approach: | They propose an efficient neural network to distill large transformers into a single smaller model. |
| Outcome: | The proposed model outperforms existing models on English datasets . it outperformed existing models with 2.7 more parameters and 2.5 slower . |
Answer Generation for Retrieval-based Question Answering Systems (2021.findings-acl)
Copied to clipboard
| Challenge: | Question Answering systems are a core component of many commercial applications . answer sentence selection (AS2) models are trained to select the best answer sentence . |
| Approach: | They propose to train a sequence to sequence transformer model to generate an answer from a set of candidates. |
| Outcome: | The proposed model improves accuracy by 32 points over the state-of-the-art model on English AS2 datasets. |