HotpotQA is a new dataset of 113k multi-hop Wikipedia questions with sentence-level supporting facts that enables training and evaluation of explainable QA systems.
ParlAI: A Dialog Research Software Platform
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abstract
We introduce ParlAI (pronounced "par-lay"), an open-source software platform for dialog research implemented in Python, available at http://parl.ai. Its goal is to provide a unified framework for sharing, training and testing of dialog models, integration of Amazon Mechanical Turk for data collection, human evaluation, and online/reinforcement learning; and a repository of machine learning models for comparing with others' models, and improving upon existing architectures. Over 20 tasks are supported in the first release, including popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, QACNN, QADailyMail, CBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA. Several models are integrated, including neural models such as memory networks, seq2seq and attentive LSTMs.
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UNVERDICTED 2representative citing papers
A dual hierarchical RL framework with two agents coordinates high-level dialogue strategy and low-level question generation to emulate judicial questioning and extract key information from Supreme Court arguments, outperforming baselines.
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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
HotpotQA is a new dataset of 113k multi-hop Wikipedia questions with sentence-level supporting facts that enables training and evaluation of explainable QA systems.
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Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents
A dual hierarchical RL framework with two agents coordinates high-level dialogue strategy and low-level question generation to emulate judicial questioning and extract key information from Supreme Court arguments, outperforming baselines.