Papers by Matteo Gabburo
Knowledge Transfer from Answer Ranking to Answer Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences. |
| Approach: | They propose to train a GenQA model by transferring knowledge from a trained AS2 model . they use top ranked candidate as the generation target and next k top rated candidates as context . |
| Outcome: | The proposed model outperforms existing models on public and industrial datasets. |
Measuring Retrieval Complexity in Question Answering Systems (2024.findings-acl)
Copied to clipboard
| Challenge: | a new metric, retrieval complexity (RC), measures the difficulty of answering questions. |
| Approach: | They propose a retrieval complexity metric conditioned on the completeness of retrieved documents . they propose an unsupervised pipeline to measure RC given an arbitrary retrieval system . |
| Outcome: | The proposed pipeline measures RC more accurately than alternative estimators on six challenging QA benchmarks. |
Effective Pretraining Objectives for Transformer-based Autoencoders (2022.findings-emnlp)
Copied to clipboard
| Challenge: | ELECTRA is more accurate than BERT, but it is not clear if this is due to its innovative architecture or to the long and extensive training, which highly increases the computation cost for obtaining the final language model. |
| Approach: | They propose to replace BERT’s Masked Language Modeling objective (MLM) with Token Detection (TD) by using a statistical approach to generate light tokens. |
| Outcome: | The proposed method can replace ELECTRA's computationally heavy generators without a significant drop in performance. |
Learning Answer Generation using Supervision from Automatic Question Answering Evaluators (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies show sentence-level extractive QA is outperformed by Generation-based QA (GenQA) models. |
| Approach: | They propose a training paradigm for GenQA using automatic QA evaluation models . they augment training data with answers generated by the GenQA model and labelled by GAVA . |
| Outcome: | The proposed training paradigm improves answering accuracy over existing models. |
Datasets for Multilingual Answer Sentence Selection (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing datasets for Answer Sentence Selection (AS2) focus on English due to the scarcity of annotated datasets. |
| Approach: | They propose to use a large language model to train answer sentences in English . they use annotated datasets from English and other languages to train AS2 models . |
| Outcome: | The proposed datasets are highly performant and close the performance gap between English and other languages. |