13 research outputs found
Deep Active Learning for Dialogue Generation
We propose an online, end-to-end, neural generative conversational model for
open-domain dialogue. It is trained using a unique combination of offline
two-phase supervised learning and online human-in-the-loop active learning.
While most existing research proposes offline supervision or hand-crafted
reward functions for online reinforcement, we devise a novel interactive
learning mechanism based on hamming-diverse beam search for response generation
and one-character user-feedback at each step. Experiments show that our model
inherently promotes the generation of semantically relevant and interesting
responses, and can be used to train agents with customized personas, moods and
conversational styles.Comment: Accepted at 6th Joint Conference on Lexical and Computational
Semantics (*SEM) 2017 (Previously titled "Online Sequence-to-Sequence Active
Learning for Open-Domain Dialogue Generation" on ArXiv
Emotion-Aware and Human-Like Autonomous Agents
In human-computer interaction (HCI), one of the technological goals is to build human-like artificial agents that can think, decide and behave like humans during the interaction. A prime example is a dialogue system, where the agent should converse fluently and coherently with a user and connect with them emotionally. Humanness and emotion-awareness of interactive artificial agents have been shown to improve user experience and help attain application-specific goals more quickly. However, achieving human-likeness in HCI systems is contingent on addressing several philosophical and scientific challenges. In this thesis, I address two such challenges: replicating the human ability to 1) correctly perceive and adopt emotions, and 2) communicate effectively through language.
Several research studies in neuroscience, economics, psychology and sociology show that both language and emotional reasoning are essential to the human cognitive deliberation process. These studies establish that any human-like AI should necessarily be equipped with adequate emotional and linguistic cognizance. To this end, I explore the following research directions.
- I study how agents can reason emotionally in various human-interactive settings for decision-making. I use Bayesian Affect Control Theory, a probabilistic model of human-human affective interactions, to build a decision-theoretic reasoning algorithm about affect. This approach is validated on several applications: two-person social dilemma games, an assistive healthcare device, and robot navigation.
- I develop several techniques to understand and generate emotions/affect in language. The proposed methods include affect-based feature augmentation of neural conversational models, training regularization using affective objectives, and affectively diverse sequential inference.
- I devise an active learning technique that elicits user feedback during a conversation. This enables the agent to learn in real time, and to produce natural and coherent language during the interaction.
- I explore incremental domain adaptation in language classification and generation models. The proposed method seeks to replicate the human ability to continually learn from new environments without forgetting old experiences
ConvGenVisMo: Evaluation of Conversational Generative Vision Models
Conversational generative vision models (CGVMs) like Visual ChatGPT (Wu et
al., 2023) have recently emerged from the synthesis of computer vision and
natural language processing techniques. These models enable more natural and
interactive communication between humans and machines, because they can
understand verbal inputs from users and generate responses in natural language
along with visual outputs. To make informed decisions about the usage and
deployment of these models, it is important to analyze their performance
through a suitable evaluation framework on realistic datasets. In this paper,
we present ConvGenVisMo, a framework for the novel task of evaluating CGVMs.
ConvGenVisMo introduces a new benchmark evaluation dataset for this task, and
also provides a suite of existing and new automated evaluation metrics to
evaluate the outputs. All ConvGenVisMo assets, including the dataset and the
evaluation code, will be made available publicly on GitHub
Towards Knowledge-Based Personalized Product Description Generation in E-commerce
Quality product descriptions are critical for providing competitive customer
experience in an e-commerce platform. An accurate and attractive description
not only helps customers make an informed decision but also improves the
likelihood of purchase. However, crafting a successful product description is
tedious and highly time-consuming. Due to its importance, automating the
product description generation has attracted considerable interests from both
research and industrial communities. Existing methods mainly use templates or
statistical methods, and their performance could be rather limited. In this
paper, we explore a new way to generate the personalized product description by
combining the power of neural networks and knowledge base. Specifically, we
propose a KnOwledge Based pErsonalized (or KOBE) product description generation
model in the context of e-commerce. In KOBE, we extend the encoder-decoder
framework, the Transformer, to a sequence modeling formulation using
self-attention. In order to make the description both informative and
personalized, KOBE considers a variety of important factors during text
generation, including product aspects, user categories, and knowledge base,
etc. Experiments on real-world datasets demonstrate that the proposed method
out-performs the baseline on various metrics. KOBE can achieve an improvement
of 9.7% over state-of-the-arts in terms of BLEU. We also present several case
studies as the anecdotal evidence to further prove the effectiveness of the
proposed approach. The framework has been deployed in Taobao, the largest
online e-commerce platform in China.Comment: KDD 2019 Camera-ready. Website:
https://sites.google.com/view/kobe201