14,173 research outputs found
Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering
In this paper, we focus on the problem of answer triggering ad-dressed by
Yang et al. (2015), which is a critical component for a real-world question
answering system. We employ a hierarchical gated recurrent neural tensor
(HGRNT) model to capture both the context information and the deep
in-teractions between the candidate answers and the question. Our result on F
val-ue achieves 42.6%, which surpasses the baseline by over 10 %
Hashing based Answer Selection
Answer selection is an important subtask of question answering (QA), where
deep models usually achieve better performance. Most deep models adopt
question-answer interaction mechanisms, such as attention, to get vector
representations for answers. When these interaction based deep models are
deployed for online prediction, the representations of all answers need to be
recalculated for each question. This procedure is time-consuming for deep
models with complex encoders like BERT which usually have better accuracy than
simple encoders. One possible solution is to store the matrix representation
(encoder output) of each answer in memory to avoid recalculation. But this will
bring large memory cost. In this paper, we propose a novel method, called
hashing based answer selection (HAS), to tackle this problem. HAS adopts a
hashing strategy to learn a binary matrix representation for each answer, which
can dramatically reduce the memory cost for storing the matrix representations
of answers. Hence, HAS can adopt complex encoders like BERT in the model, but
the online prediction of HAS is still fast with a low memory cost. Experimental
results on three popular answer selection datasets show that HAS can outperform
existing models to achieve state-of-the-art performance
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
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