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Found 8 papers, 3 papers with code
Automatic Evaluation vs. User Preference in Neural Textual QuestionAnswering over COVID-19 Scientific Literature
In this paper we present a quantitative and qualitative analysis of the system.
Training Language Models with Language Feedback
We learn from language feedback on model outputs using a three-step learning algorithm.
Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning
The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility.
Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems
In this work, we introduce \emph{Spot The Bot}, a cost-efficient and robust evaluation framework that replaces human-bot conversations with conversations between bots.
DoQA - Accessing Domain-Specific FAQs via Conversational QA
We present DoQA, a dataset with 2, 437 dialogues and 10, 917 QA pairs.
DoQA -- Accessing Domain-Specific FAQs via Conversational QA
We present DoQA, a dataset with 2, 437 dialogues and 10, 917 QA pairs.
Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque
Conversational Question Answering (CQA) systems meet user information needs by having conversations with them, where answers to the questions are retrieved from text.
Give your Text Representation Models some Love: the Case for Basque
This is suboptimal as, for many languages, the models have been trained on smaller (or lower quality) corpora.
