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Lazaros Polymenakos

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2 papers

1 author row

AAAI Conference 2018 System Paper

A Unified Implicit Dialog Framework for Conversational Commerce

Song Feng
R. Chulaka Gunasekara
Sunil Shashidhara
Kshitij Fadnis
Lazaros Polymenakos

We propose a uniﬁed Implicit Dialog framework for goaloriented, information seeking tasks of Conversational Commerce applications. It aims to enable the dialog interactions with domain data without replying on the explicitly encoded rules but utilizing the underlying data representation to build the components required for the interactions, which we refer as Implicit Dialog in this work. The proposed framework consists of a pipeline of End-to-End trainable modules. It generates a centralized knowledge representation to semantically ground multiple sub-modules. The framework is also integrated with an associated set of tools to gather end users’ input for continuous improvement of the system. This framework is designed to facilitate fast development of conversational systems by identifying the components and the data that can be adapted and reused across many end-user applications. We demonstrate our approach by creating conversational agents for several independent domains.

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AAAI Conference 2018 Conference Paper

Addressee and Response Selection in Multi-Party Conversations With Speaker Interaction RNNs

Rui Zhang
Honglak Lee
Lazaros Polymenakos
Dragomir Radev

In this paper, we study the problem of addressee and response selection in multi-party conversations. Understanding multi-party conversations is challenging because of complex speaker interactions: multiple speakers exchange messages with each other, playing different roles (sender, addressee, observer), and these roles vary across turns. To tackle this challenge, we propose the Speaker Interaction Recurrent Neural Network (SI-RNN). Whereas the previous stateof-the-art system updated speaker embeddings only for the sender, SI-RNN uses a novel dialog encoder to update speaker embeddings in a role-sensitive way. Additionally, unlike the previous work that selected the addressee and response separately, SI-RNN selects them jointly by viewing the task as a sequence prediction problem. Experimental results show that SI-RNN signiﬁcantly improves the accuracy of addressee and response selection, particularly in complex conversations with many speakers and responses to distant messages many turns in the past.

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