3,576 research outputs found
Exploring the context of recurrent neural network based conversational agents
Conversational agents have begun to rise both in the academic (in terms of
research) and commercial (in terms of applications) world. This paper
investigates the task of building a non-goal driven conversational agent, using
neural network generative models and analyzes how the conversation context is
handled. It compares a simpler Encoder-Decoder with a Hierarchical Recurrent
Encoder-Decoder architecture, which includes an additional module to model the
context of the conversation using previous utterances information. We found
that the hierarchical model was able to extract relevant context information
and include them in the generation of the output. However, it performed worse
(35-40%) than the simple Encoder-Decoder model regarding both grammatically
correct output and meaningful response. Despite these results, experiments
demonstrate how conversations about similar topics appear close to each other
in the context space due to the increased frequency of specific topic-related
words, thus leaving promising directions for future research and how the
context of a conversation can be exploited.Comment: Accepted at ICAART 2019, 10 page
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
We investigate the task of building open domain, conversational dialogue
systems based on large dialogue corpora using generative models. Generative
models produce system responses that are autonomously generated word-by-word,
opening up the possibility for realistic, flexible interactions. In support of
this goal, we extend the recently proposed hierarchical recurrent
encoder-decoder neural network to the dialogue domain, and demonstrate that
this model is competitive with state-of-the-art neural language models and
back-off n-gram models. We investigate the limitations of this and similar
approaches, and show how its performance can be improved by bootstrapping the
learning from a larger question-answer pair corpus and from pretrained word
embeddings.Comment: 8 pages with references; Published in AAAI 2016 (Special Track on
Cognitive Systems
Affect-LM: A Neural Language Model for Customizable Affective Text Generation
Human verbal communication includes affective messages which are conveyed
through use of emotionally colored words. There has been a lot of research in
this direction but the problem of integrating state-of-the-art neural language
models with affective information remains an area ripe for exploration. In this
paper, we propose an extension to an LSTM (Long Short-Term Memory) language
model for generating conversational text, conditioned on affect categories. Our
proposed model, Affect-LM enables us to customize the degree of emotional
content in generated sentences through an additional design parameter.
Perception studies conducted using Amazon Mechanical Turk show that Affect-LM
generates naturally looking emotional sentences without sacrificing grammatical
correctness. Affect-LM also learns affect-discriminative word representations,
and perplexity experiments show that additional affective information in
conversational text can improve language model prediction
Ethical Challenges in Data-Driven Dialogue Systems
The use of dialogue systems as a medium for human-machine interaction is an
increasingly prevalent paradigm. A growing number of dialogue systems use
conversation strategies that are learned from large datasets. There are well
documented instances where interactions with these system have resulted in
biased or even offensive conversations due to the data-driven training process.
Here, we highlight potential ethical issues that arise in dialogue systems
research, including: implicit biases in data-driven systems, the rise of
adversarial examples, potential sources of privacy violations, safety concerns,
special considerations for reinforcement learning systems, and reproducibility
concerns. We also suggest areas stemming from these issues that deserve further
investigation. Through this initial survey, we hope to spur research leading to
robust, safe, and ethically sound dialogue systems.Comment: In Submission to the AAAI/ACM conference on Artificial Intelligence,
Ethics, and Societ
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