Papers by Gustavo Hernandez Abrego
Self-Supervised Learning for Pairwise Data Refinement (2020.aacl-main)
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| Challenge: | Pairwise data constructed from weakly supervised signals is widely used for training deep learning models. |
| Approach: | They propose two methods to refine pairwise data that are aimed to obtain subsets that are more useful as learning examples. |
| Outcome: | The proposed methods achieve most machine translation gains in the first iteration, but following iterations further improve its intrinsic evaluation. |
Multilingual Universal Sentence Encoder for Semantic Retrieval (2020.acl-demos)
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Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, Steve Yuan, Chris Tar, Yun-hsuan Sung, Brian Strope, Ray Kurzweil
| Challenge: | Using a multi-task trained dual-encoder, our models embed text from 16 languages into a shared semantic space. |
| Approach: | They propose retrieval focused multilingual sentence embedding models on TensorFlow Hub. |
| Outcome: | The models achieve state-of-the-art on monolingual and cross-lingual retrieval (SR) and retrieval question answering (ReQA) competitive performance is obtained on related tasks of translation pair bitext retrieval and retrieving question answering. |
Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models (2022.findings-acl)
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| Challenge: | Sentence embeddings are useful for language processing tasks, but it is unclear how to produce them from encoder-decoder models. |
| Approach: | They investigate the effects of scaling up sentence encoders to 11B parameters on sentence embeddings from text-to-text transformers (T5) . |
| Outcome: | The proposed models outperform the previous best models on both SentEval and SentGLUE transfer tasks. |
Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval (2024.naacl-long)
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| Challenge: | et al., 2020: performance of dense retrieval models in multilingual retrieval is limited due to uneven and scarce training data available across multiple languages. |
| Approach: | They propose a synthetic retrieval training dataset containing 33 languages for fine-tuning multilingual retrievers without human supervision. |
| Outcome: | The proposed model outperforms human-supervised retrieval models on three retrieval benchmarks. |
Large Dual Encoders Are Generalizable Retrievers (2022.emnlp-main)
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Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang
| Challenge: | Experimental results show that dual encoders outperform sparse and dense retrievers on the BEIR dataset significantly. |
| Approach: | They challenge belief that bottleneck layer is too limited for out-of-domain generalization . they scale up the model while keeping bottleneck as a single dot-product with a fixed size . |
| Outcome: | The proposed model outperforms sparse and dense retrievers on the BEIR dataset significantly. |
SamToNe: Improving Contrastive Loss for Dual Encoder Retrieval Models with Same Tower Negatives (2023.findings-acl)
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| Challenge: | Dual encoders have been used for retrieval tasks and representation learning with good results. |
| Approach: | They propose an improved contrastive learning objective by adding queries or documents from the same encoder towers to the negatives. |
| Outcome: | The proposed model improves retrieval quality for both symmetric and asymmetric dual encoders by adding queries or documents from the same encoder towers to the negatives. |
Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval (2021.emnlp-main)
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| Challenge: | Existing neural firststage retrieval models overcome lexical gap issue by projecting query and document to a shared dense space. |
| Approach: | They propose a multi-stage framework for neural passage retrieval using synthetic data, negative sampling, and fusion techniques. |
| Outcome: | The proposed framework improves retrieval accuracy and enhances the negative contrast in both stages. |