5,119 research outputs found
Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture
We describe a new deep learning architecture for learning to rank question
answer pairs. Our approach extends the long short-term memory (LSTM) network
with holographic composition to model the relationship between question and
answer representations. As opposed to the neural tensor layer that has been
adopted recently, the holographic composition provides the benefits of scalable
and rich representational learning approach without incurring huge parameter
costs. Overall, we present Holographic Dual LSTM (HD-LSTM), a unified
architecture for both deep sentence modeling and semantic matching.
Essentially, our model is trained end-to-end whereby the parameters of the LSTM
are optimized in a way that best explains the correlation between question and
answer representations. In addition, our proposed deep learning architecture
requires no extensive feature engineering. Via extensive experiments, we show
that HD-LSTM outperforms many other neural architectures on two popular
benchmark QA datasets. Empirical studies confirm the effectiveness of
holographic composition over the neural tensor layer.Comment: SIGIR 2017 Full Pape
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
Matching natural language sentences is central for many applications such as
information retrieval and question answering. Existing deep models rely on a
single sentence representation or multiple granularity representations for
matching. However, such methods cannot well capture the contextualized local
information in the matching process. To tackle this problem, we present a new
deep architecture to match two sentences with multiple positional sentence
representations. Specifically, each positional sentence representation is a
sentence representation at this position, generated by a bidirectional long
short term memory (Bi-LSTM). The matching score is finally produced by
aggregating interactions between these different positional sentence
representations, through -Max pooling and a multi-layer perceptron. Our
model has several advantages: (1) By using Bi-LSTM, rich context of the whole
sentence is leveraged to capture the contextualized local information in each
positional sentence representation; (2) By matching with multiple positional
sentence representations, it is flexible to aggregate different important
contextualized local information in a sentence to support the matching; (3)
Experiments on different tasks such as question answering and sentence
completion demonstrate the superiority of our model.Comment: Accepted by AAAI-201
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
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