Papers by Shruti Bhargava
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues (2021.naacl-main)
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Bo-Hsiang Tseng, Shruti Bhargava, Jiarui Lu, Joel Ruben Antony Moniz, Dhivya Piraviperumal, Lin Li, Hong Yu
| Challenge: | Traditionally, anaphora resolution and ellipses resolution are limited in dialogues . despite rapid progress in dialogue systems, several difficulties remain . |
| Approach: | They propose a joint learning framework for modeling coreference resolution and query rewriting for complex, multi-turn dialogues. |
| Outcome: | The proposed model outperforms the state-of-the-art model on a rewritten dialogue dataset. |
Can Large Language Models Understand Context? (2024.findings-eacl)
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Yilun Zhu, Joel Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng
| Challenge: | Existing evaluation methodologies for Large Language Models (LLMs) have been inadequate to evaluate their ability to understand contextual features. |
| Approach: | They propose a benchmark to assess large language models' ability to understand context by adapting existing datasets to suit their evaluation. |
| Outcome: | The proposed model performs better under the in-context learning pretraining scenario than state-of-the-art models. |
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking (2024.eacl-long)
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| Challenge: | In-context learning with Large Language Models (LLMs) is a promising avenue of research in Dialog State Tracking (DST). |
| Approach: | They propose a data generation framework tailored for Dialog State Tracking that uses large language models to synthesize natural, coherent, and free-flowing dialogues with DST annotations. |
| Outcome: | The proposed framework improves joint goal accuracy by 4-5% over the zero-shot baseline on MultiWOZ 2.1 and 2.4. |
Conversational Semantic Parsing for Dialog State Tracking (2020.emnlp-main)
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Jianpeng Cheng, Devang Agrawal, Héctor Martínez Alonso, Shruti Bhargava, Joris Driesen, Federico Flego, Dain Kaplan, Dimitri Kartsaklis, Lin Li, Dhivya Piraviperumal, Jason D. Williams, Hong Yu, Diarmuid Ó Séaghdha, Anders Johannsen
| Challenge: | Language understanding for task-based dialog systems is often termed "dialog state tracking" (DST) whereas semantic parsing is the task of converting a single-turn utterance to a graphstructured meaning representation, DST is more complex. |
| Approach: | They propose a framework for dialog state tracking that incorporates semantic compositionality, cross-domain knowledge sharing and co-reference. |
| Outcome: | The proposed framework improves on state-of-the-art approaches for dialog state tracking (DST) it incorporates semantic compositionality, cross-domain knowledge sharing and co-reference. |
Effective Long-Context Scaling of Foundation Models (2024.naacl-long)
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Wenhan Xiong, Jingyu Liu, Igor Molybog, Hejia Zhang, Prajjwal Bhargava, Rui Hou, Louis Martin, Rashi Rungta, Karthik Abinav Sankararaman, Barlas Oguz, Madian Khabsa, Han Fang, Yashar Mehdad, Sharan Narang, Kshitiz Malik, Angela Fan, Shruti Bhosale, Sergey Edunov, Mike Lewis, Sinong Wang, Hao Ma
| Challenge: | Large language models (LLMs) are rapidly deployed and continue to evolve through scaling. |
| Approach: | They propose a method to train strong long-context LLMs that are capable of utilizing massive context windows of up to 32,000 tokens. |
| Outcome: | The proposed model can surpass gpt-3.5-turbo-16k's overall performance on long-context benchmarks with a cost-effective instruction tuning procedure that is free of expensive annotations. |
Referring to Screen Texts with Voice Assistants (2023.acl-industry)
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Shruti Bhargava, Anand Dhoot, Ing-marie Jonsson, Hoang Long Nguyen, Alkesh Patel, Hong Yu, Vincent Renkens
| Challenge: | a new approach to voice assistants is limited in their ability to understand context of the user. |
| Approach: | They propose a general purpose model that allows users to refer to phone numbers, addresses, email addresses, urls, and dates on their phone screens. |
| Outcome: | The proposed model is lightweight, offering flexibility, better interpretability and efficient run time memory. |