Papers by Pranav Goel
How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs (C18-1)
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| Challenge: | Social media based micro-blogging sites like Twitter are used for expressing emotions and opinions. |
| Approach: | They propose to combine convolutional and fully connected layers in a non-sequential manner to train deep multi-task learning models trained for all emotions at once in unified architecture. |
| Outcome: | The proposed model outperforms the previous system by 0.044 or 4.4% on the WASSA’17 EmoInt shared task dataset. |
Studying word order through iterative shuffling (2021.emnlp-main)
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| Challenge: | Recent work on large language models has made this hypothesis popular . but, word order is not important enough to make sentence structure relevant . |
| Approach: | They propose an efficient procedure that finds word order having highest likelihood under a fixed language model. |
| Outcome: | The proposed procedure can be used to find the ordering of a bag of words having the highest likelihood under a fixed language model. |
Are Neural Topic Models Broken? (2022.findings-emnlp)
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| Challenge: | Existing evaluation paradigms are often divorced from real-world use . recent results have challenged the validity of the prevailing model evaluation paradigm . |
| Approach: | They show that neural topic models fare worse in both respects compared to an established classical method. |
| Outcome: | The proposed method outperforms the members of the ensemble in both respects. |
Improving Neural Topic Models using Knowledge Distillation (2020.emnlp-main)
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| Challenge: | Current paradigms for transfer learning use general knowledge as a foundation for more specialized endeavors. |
| Approach: | They propose to combine probabilistic topic models and pretrained transformers to improve topic quality by using knowledge distillation. |
| Outcome: | The proposed framework improves topic quality over all estimated topics and in head-to-head comparisons of aligned topics. |
How Pre-trained Word Representations Capture Commonsense Physical Comparisons (D19-60)
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| Challenge: | Pre-trained word representations capture common sense on physical properties such as size and weight. |
| Approach: | They investigate whether pre-trained representations capture comparisons and find they have higher accuracy than previous approaches. |
| Outcome: | The proposed models learn a consistent ordering over all the objects in the comparisons. |
GPT Perdetry Test: Generating new meanings for new words (2021.naacl-main)
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| Challenge: | We create a set of nonce words and prompt GPT-3 to generate their dictionary definitions. |
| Approach: | They create a set of nonce words and prompt GPT-3 to generate their dictionary definitions. |
| Outcome: | The proposed model can process new words and make them 'neologisms' . it can also adapt to and extend a changing vocabulary, the authors found . |
Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis (2024.eacl-long)
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Zongxia Li, Andrew Mao, Daniel Stephens, Pranav Goel, Emily Walpole, Alden Dima, Juan Fung, Jordan Boyd-Graber
| Challenge: | Existing evaluation metrics such as coherence and coherency are inadequate for neural topic models. |
| Approach: | They conduct the first evaluation of neural, supervised and classical topic models in an interactive task-based setting. |
| Outcome: | The proposed model performs better on cluster evaluation metrics and human evaluations than classical models on real-world tasks. |
Natural Language Decompositions of Implicit Content Enable Better Text Representations (2023.emnlp-main)
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| Challenge: | a new method for the analysis of text takes implicitly communicated content into account . authors: human interpretation of each individual utterance is intractable . |
| Approach: | They propose a method that takes explicit communication into account when people interpret text . they use a large language model to generate propositions that are inferentially related to the text analyzed . |
| Outcome: | The proposed method proves useful in multiple problems that involve interpretation of utterances . it uncovers high-level narratives in public commentary, which are often not expressed in surface forms . |
Sarcasm Target Identification: Dataset and An Introductory Approach (L18-1)
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| Challenge: | Past work on sarcasm detection has focused on identifying the sarcasm target of ridicule in a sarkastic text. |
| Approach: | They propose a task of extracting the sarcastic target of ridicule from a sarcastical text using a manually annotated dataset and an automatic approach. |
| Outcome: | The proposed approach establishes the viability of sarcasm target identification and will serve as a baseline for future work. |
Measuring scalar constructs in social science with LLMs (2025.emnlp-main)
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Hauke Licht, Rupak Sarkar, Patrick Y. Wu, Pranav Goel, Niklas Stoehr, Elliott Ash, Alexander Miserlis Hoyle
| Challenge: | Valid scalar measurement of skalar constructs is a fundamental task in text analysis. |
| Approach: | They evaluate four approaches to measuring scalar constructs using large language models . pairwise comparisons produced better measurements than prompting LLMs, they say . validation of skalar measurement enables wide range of substantive applications in social science research . |
| Outcome: | The proposed methods improve on pairwise comparisons and finetuning . the proposed methods can be used in social science research . |