THESIS
2017
xi, 68 pages : illustrations ; 30 cm
Abstract
Nowadays, chatbots, or dialogue systems, become quite popular and lots of companies invest
large amounts of money on them. Chatbots can be divided into two categories, namely open-domain
bots and task-oriented bots. The big challenge in open-domain chatbots is that the domain
is not limited. As for task-oriented chatbots, they focus on a particular domain such as booking
flight tickets, etc.
Question answering (QA) in dialogue can be treated as a single-turn conversation. Two approaches
are applied to produce answers, namely, retrieval-based approach and generation-based
approach.
Retrieval-based question answering(QA) aims to select an appropriate answer from a predefined
repository of QA according to a user’s question. Pervious research usually employs one kind
of discrimina...[
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Nowadays, chatbots, or dialogue systems, become quite popular and lots of companies invest
large amounts of money on them. Chatbots can be divided into two categories, namely open-domain
bots and task-oriented bots. The big challenge in open-domain chatbots is that the domain
is not limited. As for task-oriented chatbots, they focus on a particular domain such as booking
flight tickets, etc.
Question answering (QA) in dialogue can be treated as a single-turn conversation. Two approaches
are applied to produce answers, namely, retrieval-based approach and generation-based
approach.
Retrieval-based question answering(QA) aims to select an appropriate answer from a predefined
repository of QA according to a user’s question. Pervious research usually employs one kind
of discriminative model such as dual encoder based neural network to improve the performance
of QA classification, commonly resulting in overfitting. To deal with the problem, we investigate
multi-task learning(MTL) as a regularization for retrieval-based QA, jointly training main task and auxiliary tasks with shared representations for exploiting commonalities and differences. Our main
task is a QA classification. And we design two auxiliary tasks in MTL: 1) learning sequence mapping
of actual QA pairs via sequence to sequence learning and 2) RNN language model without
relying on labeled data. Experimental results on Ubuntu Dialogue Corpus demonstrate the superiorities
of our proposed MTL method over baseline systems.
Generation-based question answering (QA), which usually based on seq2seq model, generates
answers from scratch. One problem with seq2seq model is that it will generate high-frequency and
generic answers, due to maximizing log-likelihood objective function. We investigate multi-task
learning paradigm which takes seq2seq model as the main task and the binary QA classification as
the auxiliary task. The main task and the auxiliary task are learned jointly, improving generalization
and making full use of classification labels as extra evidence to guide the answer generalization.
Experimental results on both automatic evaluations and human annotations demonstrate the superiorities
of our proposed MTL method over baselines.
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