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Understanding and Controlling Emotions and Intensity in TextAuthor: Himanshu Maheshwari Date: 2022-08-04 Report no: IIIT/TH/2022/109 Advisor:Vasudeva Varma AbstractEmotions are an essential part of daily communication and play a critical role in conveying a person’s mental state. Understanding, categorization, and controllable emotions generation are widely researched in NLP and social sciences. Different work in social sciences aimed to define emotions and identify different emotions. Ekman’s initial work identified six primary emotions: anger, disgust, fear, joy, sadness, and surprise. Plutchik identified eight emotions and classified them into four polar opposite pairs. With the advent of computer science, different computational approaches have been developed to identify and categorize emotions and generate emotional text. In this thesis, we develop data-driven, psychologically grounded computational approaches to solve two broad problems relating to emotions in text. The first problem is identifying emotions at a paragraph level. Existing work detects emotion at a sentence level having a limited context. We aim to detect emotions at a paragraph level consisting of multiple sentences. Thus, the model needs to consider the emotional cues present in an individual sentence and paragraph as a whole unit. We also explore the classification problem in scientific documents when the context is large but cannot be broken down into individual sentences. The second problem that we explore is changing the emotion of a sentence while controlling its intensity. In the process, the meaning of the sentence should be preserved. This task of changing specific attributes of a sentence while preserving its overall meaning is defined as style transfer. For both the tasks, the proposed system achieved state-of-the-art. Pretrained language models are current de facto in Natural Language Processing (NLP). We develop an ensemble solution consisting of three finetuned language models for the first task. The language models are RoBERTa, BART, and RoBERTa model that is first finetuned on sentence-level emotion detection tasks. RoBERTa model is used due to its remarkable performance in many natural language understanding tasks. BART has shown good performance for tasks involving multi-sentence level context like summarization, thus is used here. RoBERTa model that is first finetuned on sentence-level emotion detection tasks is used to identify strong sentence-level cues. Our proposed system was ranked one globally for the Empathy Detection and Emotion Classification shared task at the WASSA workshop at ACL 2022. We also explore the power of the pretrained language model for the classification of scientific documents when the context is large but cannot be divided into sentences. Our proposed system of using highly contextualized embeddings of science BERT achieved a new state of the art and was ranked one globally for 3C Citation Context Classification shared task at the Scholarly Document Processing workshop at NAACL 2021. Full thesis: pdf Centre for Language Technologies Research Centre |
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