Papers by Livio Soares
Evaluating and Modeling Attribution for Cross-Lingual Question Answering (2023.emnlp-main)
Copied to clipboard
Benjamin Muller, John Wieting, Jonathan Clark, Tom Kwiatkowski, Sebastian Ruder, Livio Soares, Roee Aharoni, Jonathan Herzig, Xinyi Wang
| Challenge: | Open-retrieval question answering systems are lacking in attribution for cross-lingual question answering . open-research questions are available in 20 languages, but their raw generation often falls short in factuality . |
| Approach: | They are the first to study attribution for cross-lingual question answering . they collect data in 5 languages to assess the attribution level of a state-of-the-art QA system . |
| Outcome: | The proposed approach improves the attribution level of a state-of-the-art cross-lingual QA system. |
NAIL: Lexical Retrieval Indices with Efficient Non-Autoregressive Decoders (2023.emnlp-main)
Copied to clipboard
| Challenge: | Neural document rerankers require dedicated hardware for serving, which is costly and often not feasible. |
| Approach: | They propose a method that captures 86% of the gains of a Transformer cross-attention model with a lexicalized scoring function that only requires 10-6% of . the model architecture is compatible with recent encoder-decoder and decoder-only large language models, such as T5, GPT-3 and PaLM. |
| Outcome: | The proposed model captures 86% of the gains of a Transformer cross-attention model with a lexicalized scoring function. |
1-PAGER: One Pass Answer Generation and Evidence Retrieval (2023.findings-emnlp)
Copied to clipboard
| Challenge: | 1-Pager is the first system that answers a question and retrieves evidence using a single Transformer-based model and decoding process. |
| Approach: | They propose a system that partitions the corpus using constrained decoding to select a document and answer string, and a method that uses a single Transformer-based model to generate evidence. |
| Outcome: | The proposed system outperforms the equivalent ‘closed-book’ question answering model by grounding predictions in evidence corpus. |