Angeliki Metallinou


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Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents
Nikolaos Malandrakis | Minmin Shen | Anuj Goyal | Shuyang Gao | Abhishek Sethi | Angeliki Metallinou
Proceedings of the 3rd Workshop on Neural Generation and Translation

Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents. We investigate the use of text generation techniques to augment the training data of a popular commercial artificial agent across categories of functionality, with the goal of faster development of new functionality. We explore a variety of encoder-decoder generative models for synthetic training data generation and propose using conditional variational auto-encoders. Our approach requires only direct optimization, works well with limited data and significantly outperforms the previous controlled text generation techniques. Further, the generated data are used as additional training samples in an extrinsic intent classification task, leading to improved performance by up to 5% absolute f-score in low-resource cases, validating the usefulness of our approach.

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Simple Question Answering with Subgraph Ranking and Joint-Scoring
Wenbo Zhao | Tagyoung Chung | Anuj Goyal | Angeliki Metallinou
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the literature has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject–relation dependency of a KB fact have not been sufficiently explored. Motivated by this, we present a unified framework to describe and analyze existing approaches. Using this framework as a starting point we focus on two aspects: improving subgraph selection through a novel ranking method, and leveraging the subject–relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores. Our methods achieve a new state of the art (85.44% in accuracy) on the SimpleQuestions dataset.


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Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents
Anuj Kumar Goyal | Angeliki Metallinou | Spyros Matsoukas
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

Fast expansion of natural language functionality of intelligent virtual agents is critical for achieving engaging and informative interactions. However, developing accurate models for new natural language domains is a time and data intensive process. We propose efficient deep neural network architectures that maximally re-use available resources through transfer learning. Our methods are applied for expanding the understanding capabilities of a popular commercial agent and are evaluated on hundreds of new domains, designed by internal or external developers. We demonstrate that our proposed methods significantly increase accuracy in low resource settings and enable rapid development of accurate models with less data.


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Syllable and language model based features for detecting non-scorable tests in spoken language proficiency assessment applications
Angeliki Metallinou | Jian Cheng
Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications


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Discriminative state tracking for spoken dialog systems
Angeliki Metallinou | Dan Bohus | Jason Williams
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)