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Generation model (Pegasus fine-tuned with QReCC) used in the participation of group Rachael for SCAI 2021.

GitHub repository can be found in: gonced8/rachael-scai

Gonçalo Raposo


  author     = {Gonçalo Raposo and Rui Ribeiro and Bruno Martins and Luísa Coheur},
  booktitle  = {44th European Conference on Information Retrieval},
  title      = {Question rewriting? Assessing its importance for conversational question answering},
  year       = {2022},
  month      = apr,
  note       = {This version of the contribution has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/[not yet available]. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use \url{https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms}},
  abstract   = {In conversational question answering, systems must correctly interpret the interconnected interactions and generate knowledgeable answers, which may require the retrieval of relevant information from a background repository. Recent approaches to this problem leverage neural language models, although different alternatives can be considered in terms of modules for (a) representing user questions in context, (b) retrieving the relevant background information, and (c) generating the answer. This work presents a conversational question answering system designed specifically for the Search-Oriented Conversational AI (SCAI) shared task, and reports on a detailed analysis of its question rewriting module. In particular, we considered different variations of the question rewriting module to evaluate the influence on the subsequent components, and performed a careful analysis of the results obtained with the best system configuration. Our system achieved the best performance in the shared task and our analysis emphasizes the importance of the conversation context representation for the overall system performance.},
  keywords   = {conversational question answering, conversational search, question rewriting, transformer-based neural language models},
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