Aniruddha Tammewar


2022

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Can Emotion Carriers Explain Automatic Sentiment Prediction? A Study on Personal Narratives
Seyed Mahed Mousavi | Gabriel Roccabruna | Aniruddha Tammewar | Steve Azzolin | Giuseppe Riccardi
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis

Deep Neural Networks (DNN) models have achieved acceptable performance in sentiment prediction of written text. However, the output of these machine learning (ML) models cannot be natively interpreted. In this paper, we study how the sentiment polarity predictions by DNNs can be explained and compare them to humans’ explanations. We crowdsource a corpus of Personal Narratives and ask human judges to annotate them with polarity and select the corresponding token chunks - the Emotion Carriers (EC) - that convey narrators’ emotions in the text. The interpretations of ML neural models are carried out through Integrated Gradients method and we compare them with human annotators’ interpretations. The results of our comparative analysis indicate that while the ML model mostly focuses on the explicit appearance of emotions-laden words (e.g. happy, frustrated), the human annotator predominantly focuses the attention on the manifestation of emotions through ECs that denote events, persons, and objects which activate narrator’s emotional state.

2020

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Annotation of Emotion Carriers in Personal Narratives
Aniruddha Tammewar | Alessandra Cervone | Eva-Maria Messner | Giuseppe Riccardi
Proceedings of the 12th Language Resources and Evaluation Conference

We are interested in the problem of understanding personal narratives (PN) - spoken or written - recollections of facts, events, and thoughts. For PNs, we define emotion carriers as the speech or text segments that best explain the emotional state of the narrator. Such segments may span from single to multiple words, containing for example verb or noun phrases. Advanced automatic understanding of PNs requires not only the prediction of the narrator’s emotional state but also to identify which events (e.g. the loss of a relative or the visit of grandpa) or people (e.g. the old group of high school mates) carry the emotion manifested during the personal recollection. This work proposes and evaluates an annotation model for identifying emotion carriers in spoken personal narratives. Compared to other text genres such as news and microblogs, spoken PNs are particularly challenging because a narrative is usually unstructured, involving multiple sub-events and characters as well as thoughts and associated emotions perceived by the narrator. In this work, we experiment with annotating emotion carriers in speech transcriptions from the Ulm State-of-Mind in Speech (USoMS) corpus, a dataset of PNs in German. We believe this resource could be used for experiments in the automatic extraction of emotion carriers from PN, a task that could provide further advancements in narrative understanding.

2014

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SEECAT: ASR & Eye-tracking enabled computer-assisted translation
Mercedes García-Martínez | Karan Singla | Aniruddha Tammewar | Bartolomé Mesa-Lao | Ankita Thakur | Anusuya M.A. | Srinivas Bangalore | Michael Carl
Proceedings of the 17th Annual conference of the European Association for Machine Translation

2013

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Exploring Semantic Information in Hindi WordNet for Hindi Dependency Parsing
Sambhav Jain | Naman Jain | Aniruddha Tammewar | Riyaz Ahmad Bhat | Dipti Sharma
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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Two-stage Approach for Hindi Dependency Parsing Using MaltParser
Naman Jain | Karan Singla | Aniruddha Tammewar | Sambhav Jain
Proceedings of the Workshop on Machine Translation and Parsing in Indian Languages