6 research outputs found
A Conditional Generative Chatbot using Transformer Model
A Chatbot serves as a communication tool between a human user and a machine
to achieve an appropriate answer based on the human input. In more recent
approaches, a combination of Natural Language Processing and sequential models
are used to build a generative Chatbot. The main challenge of these models is
their sequential nature, which leads to less accurate results. To tackle this
challenge, in this paper, a novel end-to-end architecture is proposed using
conditional Wasserstein Generative Adversarial Networks and a transformer model
for answer generation in Chatbots. While the generator of the proposed model
consists of a full transformer model to generate an answer, the discriminator
includes only the encoder part of a transformer model followed by a classifier.
To the best of our knowledge, this is the first time that a generative Chatbot
is proposed using the embedded transformer in both generator and discriminator
models. Relying on the parallel computing of the transformer model, the results
of the proposed model on the Cornell Movie-Dialog corpus and the Chit-Chat
datasets confirm the superiority of the proposed model compared to
state-of-the-art alternatives using different evaluation metrics
CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts
Rationality and emotion are two fundamental elements of humans. Endowing
agents with rationality and emotion has been one of the major milestones in AI.
However, in the field of conversational AI, most existing models only
specialize in one aspect and neglect the other, which often leads to dull or
unrelated responses. In this paper, we hypothesize that combining rationality
and emotion into conversational agents can improve response quality. To test
the hypothesis, we focus on one fundamental aspect of rationality, i.e.,
commonsense, and propose CARE, a novel model for commonsense-aware emotional
response generation. Specifically, we first propose a framework to learn and
construct commonsense-aware emotional latent concepts of the response given an
input message and a desired emotion. We then propose three methods to
collaboratively incorporate the latent concepts into response generation.
Experimental results on two large-scale datasets support our hypothesis and
show that our model can produce more accurate and commonsense-aware emotional
responses and achieve better human ratings than state-of-the-art models that
only specialize in one aspect.Comment: AAAI-202
Ranking Enhanced Dialogue Generation
How to effectively utilize the dialogue history is a crucial problem in
multi-turn dialogue generation. Previous works usually employ various neural
network architectures (e.g., recurrent neural networks, attention mechanisms,
and hierarchical structures) to model the history. However, a recent empirical
study by Sankar et al. has shown that these architectures lack the ability of
understanding and modeling the dynamics of the dialogue history. For example,
the widely used architectures are insensitive to perturbations of the dialogue
history, such as words shuffling, utterances missing, and utterances
reordering. To tackle this problem, we propose a Ranking Enhanced Dialogue
generation framework in this paper. Despite the traditional representation
encoder and response generation modules, an additional ranking module is
introduced to model the ranking relation between the former utterance and
consecutive utterances. Specifically, the former utterance and consecutive
utterances are treated as query and corresponding documents, and both local and
global ranking losses are designed in the learning process. In this way, the
dynamics in the dialogue history can be explicitly captured. To evaluate our
proposed models, we conduct extensive experiments on three public datasets,
i.e., bAbI, PersonaChat, and JDC. Experimental results show that our models
produce better responses in terms of both quantitative measures and human
judgments, as compared with the state-of-the-art dialogue generation models.
Furthermore, we give some detailed experimental analysis to show where and how
the improvements come from.Comment: Accepted at CIKM 202
Target Guided Emotion Aware Chat Machine
The consistency of a response to a given post at semantic-level and
emotional-level is essential for a dialogue system to deliver human-like
interactions. However, this challenge is not well addressed in the literature,
since most of the approaches neglect the emotional information conveyed by a
post while generating responses. This article addresses this problem by
proposing a unifed end-to-end neural architecture, which is capable of
simultaneously encoding the semantics and the emotions in a post and leverage
target information for generating more intelligent responses with appropriately
expressed emotions. Extensive experiments on real-world data demonstrate that
the proposed method outperforms the state-of-the-art methods in terms of both
content coherence and emotion appropriateness.Comment: To appear on TOIS 202