Papers by Adithya Pratapa
Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data (P18-1)
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| Challenge: | Code-mixed (CM) language training is a difficult problem because of lack of data and the increased confusability due to the presence of more than one language. |
| Approach: | They propose a computational technique for creating grammatically valid artificial CM data based on the Equivalence Constraint Theory. |
| Outcome: | The proposed method reduces the perplexity of the model and does not reduce the perceptibility of the models. |
Background Summarization of Event Timelines (2023.emnlp-main)
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| Challenge: | Generating concise summaries of news events is a challenging task for newcomers to a news story. |
| Approach: | They propose a task of background news summarization that complements each timeline update with a background summary of relevant preceding events. |
| Outcome: | The proposed system performs well on a question-answering-based evaluation metric, Background Utility Score (BUS). |
Automatic Extraction of Rules Governing Morphological Agreement (2020.emnlp-main)
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Aditi Chaudhary, Antonios Anastasopoulos, Adithya Pratapa, David R. Mortensen, Zaid Sheikh, Yulia Tsvetkov, Graham Neubig
| Challenge: | Creating a descriptive grammar is an indispensable step for language documentation but it is tedious and time-consuming. |
| Approach: | They propose a framework for extracting a first-pass grammatical specification from raw text in a concise, human- and machine-readable format. |
| Outcome: | The proposed framework extracts a grammatical specification that is nearly equivalent to those created with large amounts of gold-standard annotated data. |
Constrained Fact Verification for FEVER (2020.emnlp-main)
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| Challenge: | Existing methods for fact verification rely on extracted evidence, but there is little work on understanding the reasoning process. |
| Approach: | They propose a method that enforces a closed-world reliance on extracted evidence to verify a claim's factuality. |
| Outcome: | The proposed model outperforms existing models on the FEVER shared task and shows that it is more accurate than previous models. |
Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text Approaches (2025.naacl-short)
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| Challenge: | Summarizing large text collections is a valuable tool for document research . a multi-stage pipeline and lack of global context are challenges for large-scale summarization systems. |
| Approach: | They compare compression and full-text systems for large-scale multi-document summarization . they find that compression-based methods outperform full-context methods . |
| Outcome: | The proposed methods outperform compression-based methods on three datasets . however, they suffer information loss due to their multi-stage pipeline and lack of global context. |
Evaluating the Morphosyntactic Well-formedness of Generated Texts (2021.emnlp-main)
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Adithya Pratapa, Antonios Anastasopoulos, Shruti Rijhwani, Aditi Chaudhary, David R. Mortensen, Graham Neubig, Yulia Tsvetkov
| Challenge: | Text generation systems are ubiquitous in natural language processing applications, but evaluation of these systems remains a challenge, especially in multilingual settings. |
| Approach: | They propose a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphologically-rich rules of the language. |
| Outcome: | The proposed metric can evaluate the morphosyntactic well-formedness of text using its dependency parse and morphologically-rich rules of the language. |
Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing (2023.findings-emnlp)
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| Challenge: | CASENT predicts ultra-fine entities mentioned in text into types with calibrated confidence scores. |
| Approach: | They propose a model that predicts ultra-fine entities with calibrated confidence scores for entity typing. |
| Outcome: | The proposed model outperforms existing models in terms of F1 score and calibration error while achieving 50 times faster inference speed. |
Word Embeddings for Code-Mixed Language Processing (D18-1)
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| Challenge: | Existing bilingual word embedding techniques are not ideal for code-mixed text processing and there is a need for learning multilingual word embeds from code-mixed texts. |
| Approach: | They propose to use bilingual word embedding techniques to train skip-grams on synthetic code-mixed text generated through linguistic models of code- mixing to perform two tasks. |
| Outcome: | The proposed embedding technique performs better on semantic and syntactic tasks than the existing embeddable techniques on sentiment analysis and POS tagging tasks. |