13,481 research outputs found
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
Video Fill In the Blank using LR/RL LSTMs with Spatial-Temporal Attentions
Given a video and a description sentence with one missing word (we call it
the "source sentence"), Video-Fill-In-the-Blank (VFIB) problem is to find the
missing word automatically. The contextual information of the sentence, as well
as visual cues from the video, are important to infer the missing word
accurately. Since the source sentence is broken into two fragments: the
sentence's left fragment (before the blank) and the sentence's right fragment
(after the blank), traditional Recurrent Neural Networks cannot encode this
structure accurately because of many possible variations of the missing word in
terms of the location and type of the word in the source sentence. For example,
a missing word can be the first word or be in the middle of the sentence and it
can be a verb or an adjective. In this paper, we propose a framework to tackle
the textual encoding: Two separate LSTMs (the LR and RL LSTMs) are employed to
encode the left and right sentence fragments and a novel structure is
introduced to combine each fragment with an "external memory" corresponding the
opposite fragments. For the visual encoding, end-to-end spatial and temporal
attention models are employed to select discriminative visual representations
to find the missing word. In the experiments, we demonstrate the superior
performance of the proposed method on challenging VFIB problem. Furthermore, we
introduce an extended and more generalized version of VFIB, which is not
limited to a single blank. Our experiments indicate the generalization
capability of our method in dealing with such more realistic scenarios
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