Papers by Ritika Ritika
QUDeval: The Evaluation of Questions Under Discussion Discourse Parsing (2023.emnlp-main)
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| Challenge: | Existing evaluation metrics poorly approximate parser quality, says a new study . questions under discussion is a linguistic framework that views discourse as asking questions and answering them . |
| Approach: | They propose a framework for automatic evaluation of QUD parsing . they use a dataset of fine-grained evaluation of 2,190 QUD questions . |
| Outcome: | The proposed framework shows that satisfying constraints of QUD is still challenging for modern LLMs. |
CoCoa: An Encoder-Decoder Model for Controllable Code-switched Generation (2022.emnlp-main)
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| Challenge: | Generating code-switched text with fine-grained control on the degree of code-witching and the lexical choices used to convey formality has been well-explored. |
| Approach: | They propose to generate code-switched text with fine-grained control on the degree of code-changing and lexical choices used to convey formality. |
| Outcome: | The proposed model can be invoked at test-time to synthesize code-switched text faithful to syntactic and lexical attributes relevant to code-witching. |
Which questions should I answer? Salience Prediction of Inquisitive Questions (2024.emnlp-main)
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| Challenge: | Recent work in NLP has taken advantage of question generation capabilities of LLMs to enhance a wide range of applications. |
| Approach: | They propose a salience predictor for inquisitive questions that is instruction-tuned . they show that highly salient questions are empirically more likely to be answered in the same article . |
| Outcome: | The proposed model is based on linguist-annotated salience scores of 1,766 questions . it shows that answering salient questions improves comprehension of the text . |
DIMSIM: Distilled Multilingual Critics for Indic Text Simplification (2024.findings-acl)
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| Challenge: | Existing approaches to improve the quality of responses generated by large language models (LLMs) however, these critique-refine steps require multiple expensive LLM calls. |
| Approach: | They propose to use critique distillation to train critic models that are trained on input-critique pairs generated by an LLM. |
| Outcome: | The proposed model trains two separate critics that focus on lexical and structure complexity, and is more effective than using an LLM directly as a critic in both 0-shot and few-shot settings. |