Universal Transformers combine Transformer parallelism with recurrent updates and dynamic halting to achieve Turing-completeness under assumptions and outperform standard Transformers on algorithmic and language tasks.
Query-Reduction Networks for Question Answering
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abstract
In this paper, we study the problem of question answering when reasoning over multiple facts is required. We propose Query-Reduction Network (QRN), a variant of Recurrent Neural Network (RNN) that effectively handles both short-term (local) and long-term (global) sequential dependencies to reason over multiple facts. QRN considers the context sentences as a sequence of state-changing triggers, and reduces the original query to a more informed query as it observes each trigger (context sentence) through time. Our experiments show that QRN produces the state-of-the-art results in bAbI QA and dialog tasks, and in a real goal-oriented dialog dataset. In addition, QRN formulation allows parallelization on RNN's time axis, saving an order of magnitude in time complexity for training and inference.
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UNVERDICTED 2representative citing papers
K-ESIM and T-ESIM extend ESIM by incorporating domain knowledge and similar-dialog information, yielding preliminary accuracy gains on Ubuntu and Advising datasets for next-utterance selection.
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Universal Transformers
Universal Transformers combine Transformer parallelism with recurrent updates and dynamic halting to achieve Turing-completeness under assumptions and outperform standard Transformers on algorithmic and language tasks.
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Knowledge-incorporating ESIM models for Response Selection in Retrieval-based Dialog Systems
K-ESIM and T-ESIM extend ESIM by incorporating domain knowledge and similar-dialog information, yielding preliminary accuracy gains on Ubuntu and Advising datasets for next-utterance selection.