Nikolaos Malandrakis


2023

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Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users
Yohan Jo | Xinyan Zhao | Arijit Biswas | Nikoletta Basiou | Vincent Auvray | Nikolaos Malandrakis | Angeliki Metallinou | Alexandros Potamianos
Findings of the Association for Computational Linguistics: EMNLP 2023

While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively. To facilitate development of such systems, we release the Multi-User MultiWOZ dataset: task-oriented dialogues among two users and one agent. To collect this dataset, each user utterance from MultiWOZ 2.2 was replaced with a small chat between two users that is semantically and pragmatically consistent with the original user utterance, thus resulting in the same dialogue state and system response. These dialogues reflect interesting dynamics of collaborative decision-making in task-oriented scenarios, e.g., social chatter and deliberation. Supported by this data, we propose the novel task of multi-user contextual query rewriting: to rewrite a task-oriented chat between two users as a concise task-oriented query that retains only task-relevant information and that is directly consumable by the dialogue system. We demonstrate that in multi-user dialogues, using predicted rewrites substantially improves dialogue state tracking without modifying existing dialogue systems that are trained for single-user dialogues. Further, this method surpasses training a medium-sized model directly on multi-user dialogues and generalizes to unseen domains.

2019

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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.

2017

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Linguistic analysis of differences in portrayal of movie characters
Anil Ramakrishna | Victor R. Martínez | Nikolaos Malandrakis | Karan Singla | Shrikanth Narayanan
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We examine differences in portrayal of characters in movies using psycholinguistic and graph theoretic measures computed directly from screenplays. Differences are examined with respect to characters’ gender, race, age and other metadata. Psycholinguistic metrics are extrapolated to dialogues in movies using a linear regression model built on a set of manually annotated seed words. Interesting patterns are revealed about relationships between genders of production team and the gender ratio of characters. Several correlations are noted between gender, race, age of characters and the linguistic metrics.

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Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter
Athanasia Kolovou | Filippos Kokkinos | Aris Fergadis | Pinelopi Papalampidi | Elias Iosif | Nikolaos Malandrakis | Elisavet Palogiannidi | Haris Papageorgiou | Shrikanth Narayanan | Alexandros Potamianos
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter. Specifically the proposed system participated both to tweet polarity classification (two-, three- and five class) and tweet quantification (two and five-class) tasks.

2016

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Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation
Elisavet Palogiannidi | Athanasia Kolovou | Fenia Christopoulou | Filippos Kokkinos | Elias Iosif | Nikolaos Malandrakis | Haris Papageorgiou | Shrikanth Narayanan | Alexandros Potamianos
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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A quantitative analysis of gender differences in movies using psycholinguistic normatives
Anil Ramakrishna | Nikolaos Malandrakis | Elizabeth Staruk | Shrikanth Narayanan
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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SAIL-GRS: Grammar Induction for Spoken Dialogue Systems using CF-IRF Rule Similarity
Kalliopi Zervanou | Nikolaos Malandrakis | Shrikanth Narayanan
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features
Nikolaos Malandrakis | Michael Falcone | Colin Vaz | Jesse James Bisogni | Alexandros Potamianos | Shrikanth Narayanan
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

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DeepPurple: Lexical, String and Affective Feature Fusion for Sentence-Level Semantic Similarity Estimation
Nikolaos Malandrakis | Elias Iosif | Vassiliki Prokopi | Alexandros Potamianos | Shrikanth Narayanan
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

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SAIL: A hybrid approach to sentiment analysis
Nikolaos Malandrakis | Abe Kazemzadeh | Alexandros Potamianos | Shrikanth Narayanan
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)