265,545 research outputs found
Distributed Deep Learning for Question Answering
This paper is an empirical study of the distributed deep learning for
question answering subtasks: answer selection and question classification.
Comparison studies of SGD, MSGD, ADADELTA, ADAGRAD, ADAM/ADAMAX, RMSPROP,
DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Experimental results
show that the distributed framework based on the message passing interface can
accelerate the convergence speed at a sublinear scale. This paper demonstrates
the importance of distributed training. For example, with 48 workers, a 24x
speedup is achievable for the answer selection task and running time is
decreased from 138.2 hours to 5.81 hours, which will increase the productivity
significantly.Comment: This paper will appear in the Proceeding of The 25th ACM
International Conference on Information and Knowledge Management (CIKM 2016),
Indianapolis, US
Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering
In this paper, the answer selection problem in community question answering
(CQA) is regarded as an answer sequence labeling task, and a novel approach is
proposed based on the recurrent architecture for this problem. Our approach
applies convolution neural networks (CNNs) to learning the joint representation
of question-answer pair firstly, and then uses the joint representation as
input of the long short-term memory (LSTM) to learn the answer sequence of a
question for labeling the matching quality of each answer. Experiments
conducted on the SemEval 2015 CQA dataset shows the effectiveness of our
approach.Comment: 6 page
Applying Deep Learning to Answer Selection: A Study and An Open Task
We apply a general deep learning framework to address the non-factoid
question answering task. Our approach does not rely on any linguistic tools and
can be applied to different languages or domains. Various architectures are
presented and compared. We create and release a QA corpus and setup a new QA
task in the insurance domain. Experimental results demonstrate superior
performance compared to the baseline methods and various technologies give
further improvements. For this highly challenging task, the top-1 accuracy can
reach up to 65.3% on a test set, which indicates a great potential for
practical use.Comment: To appear in the proceedings of ASRU 201
A retrieval-based dialogue system utilizing utterance and context embeddings
Finding semantically rich and computer-understandable representations for
textual dialogues, utterances and words is crucial for dialogue systems (or
conversational agents), as their performance mostly depends on understanding
the context of conversations. Recent research aims at finding distributed
vector representations (embeddings) for words, such that semantically similar
words are relatively close within the vector-space. Encoding the "meaning" of
text into vectors is a current trend, and text can range from words, phrases
and documents to actual human-to-human conversations. In recent research
approaches, responses have been generated utilizing a decoder architecture,
given the vector representation of the current conversation. In this paper, the
utilization of embeddings for answer retrieval is explored by using
Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor
(ANN) model, to find similar conversations in a corpus and rank possible
candidates. Experimental results on the well-known Ubuntu Corpus (in English)
and a customer service chat dataset (in Dutch) show that, in combination with a
candidate selection method, retrieval-based approaches outperform generative
ones and reveal promising future research directions towards the usability of
such a system.Comment: A shorter version is accepted at ICMLA2017 conference;
acknowledgement added; typos correcte
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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