1,010 research outputs found
Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer
With the increasing research interest in dialogue response generation, there
is an emerging branch formulating this task as selecting next sentences, where
given the partial dialogue contexts, the goal is to determine the most probable
next sentence. Following the recent success of the Transformer model, this
paper proposes (1) a new variant of attention mechanism based on multi-head
attention, called highway attention, and (2) a recurrent model based on
transformer and the proposed highway attention, so-called Highway Recurrent
Transformer. Experiments on the response selection task in the seventh Dialog
System Technology Challenge (DSTC7) show the capability of the proposed model
of modeling both utterance-level and dialogue-level information; the
effectiveness of each module is further analyzed as well
An Empirical Study of Content Understanding in Conversational Question Answering
With a lot of work about context-free question answering systems, there is an
emerging trend of conversational question answering models in the natural
language processing field. Thanks to the recently collected datasets, including
QuAC and CoQA, there has been more work on conversational question answering,
and recent work has achieved competitive performance on both datasets. However,
to best of our knowledge, two important questions for conversational
comprehension research have not been well studied: 1) How well can the
benchmark dataset reflect models' content understanding? 2) Do the models well
utilize the conversation content when answering questions? To investigate these
questions, we design different training settings, testing settings, as well as
an attack to verify the models' capability of content understanding on QuAC and
CoQA. The experimental results indicate some potential hazards in the benchmark
datasets, QuAC and CoQA, for conversational comprehension research. Our
analysis also sheds light on both what models may learn and how datasets may
bias the models. With deep investigation of the task, it is believed that this
work can benefit the future progress of conversation comprehension. The source
code is available at https://github.com/MiuLab/CQA-Study.Comment: Published at AAAI 202
Celecoxib extends C. elegans lifespan via inhibition of insulinâlike signaling but not cyclooxygenaseâ2 activity
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86911/1/ACEL_688_sm_FigS1-S2-TableS1-S2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/86911/2/j.1474-9726.2011.00688.x.pd
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