Shrikanth Narayanan

Also published as: Shri Narayanan, Shrikanth S. Narayanan


2021

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Annotation and Evaluation of Coreference Resolution in Screenplays
Sabyasachee Baruah | Sandeep Nallan Chakravarthula | Shrikanth Narayanan
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Joint Estimation and Analysis of Risk Behavior Ratings in Movie Scripts
Victor Martinez | Krishna Somandepalli | Yalda Tehranian-Uhls | Shrikanth Narayanan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Exposure to violent, sexual, or substance-abuse content in media increases the willingness of children and adolescents to imitate similar behaviors. Computational methods that identify portrayals of risk behaviors from audio-visual cues are limited in their applicability to films in post-production, where modifications might be prohibitively expensive. To address this limitation, we propose a model that estimates content ratings based on the language use in movie scripts, making our solution available at the earlier stages of creative production. Our model significantly improves the state-of-the-art by adapting novel techniques to learn better movie representations from the semantic and sentiment aspects of a character’s language use, and by leveraging the co-occurrence of risk behaviors, following a multi-task approach. Additionally, we show how this approach can be useful to learn novel insights on the joint portrayal of these behaviors, and on the subtleties that filmmakers may otherwise not pick up on.

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Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations
Karan Singla | Zhuohao Chen | David Atkins | Shrikanth Narayanan
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Spoken language understanding tasks usually rely on pipelines involving complex processing blocks such as voice activity detection, speaker diarization and Automatic speech recognition (ASR). We propose a novel framework for predicting utterance level labels directly from speech features, thus removing the dependency on first generating transcripts, and transcription free behavioral coding. Our classifier uses a pretrained Speech-2-Vector encoder as bottleneck to generate word-level representations from speech features. This pretrained encoder learns to encode speech features for a word using an objective similar to Word2Vec. Our proposed approach just uses speech features and word segmentation information for predicting spoken utterance-level target labels. We show that our model achieves competitive results to other state-of-the-art approaches which use transcribed text for the task of predicting psychotherapy-relevant behavior codes.

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Screenplay Quality Assessment: Can We Predict Who Gets Nominated?
Ming-Chang Chiu | Tiantian Feng | Xiang Ren | Shrikanth Narayanan
Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events

Deciding which scripts to turn into movies is a costly and time-consuming process for filmmakers. Thus, building a tool to aid script selection, an initial phase in movie production, can be very beneficial. Toward that goal, in this work, we present a method to evaluate the quality of a screenplay based on linguistic cues. We address this in a two-fold approach: (1) we define the task as predicting nominations of scripts at major film awards with the hypothesis that the peer-recognized scripts should have a greater chance to succeed. (2) based on industry opinions and narratology, we extract and integrate domain-specific features into common classification techniques. We face two challenges (1) scripts are much longer than other document datasets (2) nominated scripts are limited and thus difficult to collect. However, with narratology-inspired modeling and domain features, our approach offers clear improvements over strong baselines. Our work provides a new approach for future work in screenplay analysis.

2018

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A Multi-task Approach to Learning Multilingual Representations
Karan Singla | Dogan Can | Shrikanth Narayanan
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We present a novel multi-task modeling approach to learning multilingual distributed representations of text. Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model. Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone. Our model shows competitive performance in a standard cross-lingual document classification task. We also show the effectiveness of our method in a limited resource scenario.

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NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning
Christos Baziotis | Athanasiou Nikolaos | Alexandra Chronopoulou | Athanasia Kolovou | Georgios Paraskevopoulos | Nikolaos Ellinas | Shrikanth Narayanan | Alexandros Potamianos
Proceedings of The 12th International Workshop on Semantic Evaluation

In this paper we present deep-learning models that submitted to the SemEval-2018 Task 1 competition: “Affect in Tweets”. We participated in all subtasks for English tweets. We propose a Bi-LSTM architecture equipped with a multi-layer self attention mechanism. The attention mechanism improves the model performance and allows us to identify salient words in tweets, as well as gain insight into the models making them more interpretable. Our model utilizes a set of word2vec word embeddings trained on a large collection of 550 million Twitter messages, augmented by a set of word affective features. Due to the limited amount of task-specific training data, we opted for a transfer learning approach by pretraining the Bi-LSTMs on the dataset of Semeval 2017, Task 4A. The proposed approach ranked 1st in Subtask E “Multi-Label Emotion Classification”, 2nd in Subtask A “Emotion Intensity Regression” and achieved competitive results in other subtasks.

2017

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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.

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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.

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

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A Dynamic Programming Algorithm for Computing N-gram Posteriors from Lattices
Doğan Can | 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)

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Which ASR should I choose for my dialogue system?
Fabrizio Morbini | Kartik Audhkhasi | Kenji Sagae | Ron Artstein | Doğan Can | Panayiotis Georgiou | Shri Narayanan | Anton Leuski | David Traum
Proceedings of the SIGDIAL 2013 Conference

2012

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The Twins Corpus of Museum Visitor Questions
Priti Aggarwal | Ron Artstein | Jillian Gerten | Athanasios Katsamanis | Shrikanth Narayanan | Angela Nazarian | David Traum
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The Twins corpus is a collection of utterances spoken in interactions with two virtual characters who serve as guides at the Museum of Science in Boston. The corpus contains about 200,000 spoken utterances from museum visitors (primarily children) as well as from trained handlers who work at the museum. In addition to speech recordings, the corpus contains the outputs of speech recognition performed at the time of utterance as well as the system interpretation of the utterances. Parts of the corpus have been manually transcribed and annotated for question interpretation. The corpus has been used for improving performance of the museum characters and for a variety of research projects, such as phonetic-based Natural Language Understanding, creation of conversational characters from text resources, dialogue policy learning, and research on patterns of user interaction. It has the potential to be used for research on children's speech and on language used when talking to a virtual human.

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A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle
Hao Wang | Dogan Can | Abe Kazemzadeh | François Bar | Shrikanth Narayanan
Proceedings of the ACL 2012 System Demonstrations

2009

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Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Mari Ostendorf | Michael Collins | Shri Narayanan | Douglas W. Oard | Lucy Vanderwende
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Mari Ostendorf | Michael Collins | Shri Narayanan | Douglas W. Oard | Lucy Vanderwende
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2008

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Enriching Spoken Language Translation with Dialog Acts
Vivek Kumar Rangarajan Sridhar | Srinivas Bangalore | Shrikanth Narayanan
Proceedings of ACL-08: HLT, Short Papers

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Mitigation of Data Sparsity in Classifier-Based Translation
Emil Ettelaie | Panayiotis G. Georgiou | Shrikanth S. Narayanan
Coling 2008: Proceedings of the workshop on Speech Processing for Safety Critical Translation and Pervasive Applications

2007

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Hassan: A Virtual Human for Tactical Questioning
David Traum | Antonio Roque | Anton Leuski | Panayiotis Georgiou | Jillian Gerten | Bilyana Martinovski | Shrikanth Narayanan | Susan Robinson | Ashish Vaswani
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

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Exploiting Acoustic and Syntactic Features for Prosody Labeling in a Maximum Entropy Framework
Vivek Kumar Rangarajan Sridhar | Srinivas Bangalore | Shrikanth Narayanan
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

2006

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Selecting relevant text subsets from web-data for building topic specific language models
Abhinav Sethy | Panayiotis Georgiou | Shrikanth Narayanan
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

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Text data acquisition for domain-specific language models
Abhinav Sethy | Panayiotis G. Georgiou | Shrikanth Narayanan
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

2005

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Dealing with Doctors: A Virtual Human for Non-team Interaction
David Traum | William Swartout | Jonathan Gratch | Stacy Marsella | Patrick Kenny | Eduard Hovy | Shri Narayanan | Ed Fast | Bilyana Martinovski | Rahul Baghat | Susan Robinson | Andrew Marshall | Dagen Wang | Sudeep Gandhe | Anton Leuski
Proceedings of the 6th SIGdial Workshop on Discourse and Dialogue

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Transonics: A Practical Speech-to-Speech Translator for English-Farsi Medical Dialogs
Robert Belvin | Emil Ettelaie | Sudeep Gandhe | Panayiotis Georgiou | Kevin Knight | Daniel Marcu | Scott Millward | Shrikanth Narayanan | Howard Neely | David Traum
Proceedings of the ACL Interactive Poster and Demonstration Sessions

2004

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Creation of a Doctor-Patient Dialogue Corpus Using Standardized Patients
Robert S. Melvin | Win May | Shrikanth Narayanan | Panayiotis Georgiou | Shadi Ganjavi
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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A Transcription Scheme for Languages Employing the Arabic Script Motivated by Speech Processing Applications
Shadi Ganjavi | Panayiotis G. Georgiou | Shrikanth Narayanan
Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages

2001

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Amount of Information Presented in a Complex List: Effects on User Performance
Dawn Dutton | Marilyn Walker | Selina Chu | James Hubbell | Shrikanth Narayanan
Proceedings of the First International Conference on Human Language Technology Research

1998

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Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email
Marilyn A. Walker | Jeanne C. Fromer | Shrikanth Narayanan
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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