no code implementations • EMNLP 2021 • Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay-Yoon Lee, Lizhen Tan, Lazaros Polymenakos, Andrew McCallum
It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -- a paradigm known as case-based reasoning (CBR).
Knowledge Base Question Answering Natural Language Queries +1
no code implementations • Findings of the Association for Computational Linguistics 2020 • Praveen Kumar Bodigutla, Aditya Tiwari, Josep Valls Vargas, Lazaros Polymenakos, Spyros Matsoukas
Dialogue level quality estimation is vital for optimizing data driven dialogue management.
no code implementations • WS 2020 • Longshaokan Wang, Maryam Fazel-Zarandi, Aditya Tiwari, Spyros Matsoukas, Lazaros Polymenakos
Speech-based virtual assistants, such as Amazon Alexa, Google assistant, and Apple Siri, typically convert users' audio signals to text data through automatic speech recognition (ASR) and feed the text to downstream dialog models for natural language understanding and response generation.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 18 Nov 2019 • Praveen Kumar Bodigutla, Lazaros Polymenakos, Spyros Matsoukas
To address these gaps, we created a new Response Quality annotation scheme, introduced five new domain-independent feature sets and experimented with six machine learning models to estimate User Satisfaction at both turn and dialogue level.
no code implementations • WS 2019 • Chulaka Gunasekara, Jonathan K. Kummerfeld, Lazaros Polymenakos, Walter Lasecki
Goal-oriented dialogue in complex domains is an extremely challenging problem and there are relatively few datasets.
Conversational Response Selection Goal-Oriented Dialogue Systems
1 code implementation • TACL 2019 • Janarthanan Rajendran, Jatin Ganhotra, Lazaros Polymenakos
In this work, we propose an end-to-end trainable method for neural goal-oriented dialog systems which handles new user behaviors at deployment by transferring the dialog to a human agent intelligently.
no code implementations • 11 Jan 2019 • Koichiro Yoshino, Chiori Hori, Julien Perez, Luis Fernando D'Haro, Lazaros Polymenakos, Chulaka Gunasekara, Walter S. Lasecki, Jonathan K. Kummerfeld, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan, Xiang Gao, Huda Alamari, Tim K. Marks, Devi Parikh, Dhruv Batra
This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems.
3 code implementations • ACL 2019 • Jonathan K. Kummerfeld, Sai R. Gouravajhala, Joseph Peper, Vignesh Athreya, Chulaka Gunasekara, Jatin Ganhotra, Siva Sankalp Patel, Lazaros Polymenakos, Walter S. Lasecki
Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets.
1 code implementation • EMNLP 2018 • Janarthanan Rajendran, Jatin Ganhotra, Satinder Singh, Lazaros Polymenakos
We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of goal-oriented dialog systems in a more realistic setting.
no code implementations • 23 Apr 2018 • Jatin Ganhotra, Lazaros Polymenakos
End-to-end dialog systems have become very popular because they hold the promise of learning directly from human to human dialog interaction.
1 code implementation • RANLP 2019 • Janarthanan Rajendran, Jatin Ganhotra, Xiaoxiao Guo, Mo Yu, Satinder Singh, Lazaros Polymenakos
Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources.
1 code implementation • 12 Sep 2017 • Rui Zhang, Honglak Lee, Lazaros Polymenakos, Dragomir Radev
In this paper, we study the problem of addressee and response selection in multi-party conversations.