Papers by Adria de Gispert
Neural Ranking with Weak Supervision for Open-Domain Question Answering : A Survey (2023.findings-eacl)
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| Challenge: | Neural ranking models require substantial amounts of relevance annotations, which is costly to scale. |
| Approach: | They propose to train a NR model with weak supervision instead of annotations . they use a structured overview of standard WS signals used for training a model . |
| Outcome: | The proposed approach reduces the cost of annotations by using weak supervision instead of a parametric model. |
An Inner Table Retriever for Robust Table Question Answering (2023.acl-long)
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| Challenge: | Table Question Answering (TableQA) is a task of answering NL user questions using factoid answers extracted from table content. |
| Approach: | They propose a method for handling long tables in TableQA that extracts sub-tables to preserve the most relevant information for a question. |
| Outcome: | The proposed method can improve TableQA's accuracy with up to 1.3-4.8% and achieve state-of-the-art in two benchmarks. |
LI-RAGE: Late Interaction Retrieval Augmented Generation with Explicit Signals for Open-Domain Table Question Answering (2023.acl-short)
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| Challenge: | Recent open-domain TableQA pipelines use a combination of retriever and reader . a table can be very large and might contain heterogeneous information across rows/columns . |
| Approach: | They propose to combine a retriever-reader pipeline with a binary relevance token to train the retriever and reader. |
| Outcome: | The proposed approaches improve on two open-domain TableQA datasets. |