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...[ Read more ]

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.