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Found 6 papers, 6 papers with code
MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering
Recent advances in tabular question answering (QA) with large language models are constrained in their coverage and only answer questions over a single table.
Parameter-Efficient Sparse Retrievers and Rerankers using Adapters
While previous studies have only experimented with dense retriever or in a cross lingual retrieval scenario, in this paper we aim to complete the picture on the use of adapters in IR.
Parameter-Efficient Abstractive Question Answering over Tables or Text
In this work, we study parameter-efficient abstractive QA in encoder-decoder models over structured tabular data and unstructured textual data using only 1. 5% additional parameters for each modality.
ConfNet2Seq: Full Length Answer Generation from Spoken Questions
This is the first attempt towards generating full-length natural answers from a graph input(confusion network) to the best of our knowledge.
Modeling ASR Ambiguity for Dialogue State Tracking Using Word Confusion Networks
Spoken dialogue systems typically use a list of top-N ASR hypotheses for inferring the semantic meaning and tracking the state of the dialogue.
Answering Naturally: Factoid to Full length Answer Generation
A reading comprehension system extracts a span of text, comprising of named entities, dates, small phrases, etc., which serve as the answer to a given question.
