Papers by Ankita Sharma
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations (2022.findings-acl)
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| Challenge: | Dense retrieval (DR) methods first encode texts into a dense embedding space and then conduct text retrieval using efficient nearest neighbor search. |
| Approach: | They propose Momentum adversarial Domain Invariant Representation learning to train a domain classifier that distinguishes source versus target domains and adversarially updates the DR encoder to learn domain invariant representations. |
| Outcome: | The proposed method outperforms baselines on 10+ ranking datasets collected in the BEIR benchmark in the zero-shot setting, with more than 10% relative gains on datasets with enough sensitivity for DR models’ evaluation. |
SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks (2023.acl-long)
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Suwon Shon, Siddhant Arora, Chyi-Jiunn Lin, Ankita Pasad, Felix Wu, Roshan S Sharma, Wei-Lun Wu, Hung-yi Lee, Karen Livescu, Shinji Watanabe
| Challenge: | Spoken language understanding (SLU) tasks have received little attention and resources compared to lower-level tasks like speech and speaker recognition. |
| Approach: | They propose annotated SLU benchmark tasks based on freely available speech data to complement existing benchmarks and address gaps in the evaluation landscape. |
| Outcome: | The proposed benchmarks complement existing benchmarks and address gaps in the evaluation landscape. |
On the Evaluation of Speech Foundation Models for Spoken Language Understanding (2024.findings-acl)
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Siddhant Arora, Ankita Pasad, Chung-Ming Chien, Jionghao Han, Roshan Sharma, Jee-weon Jung, Hira Dhamyal, William Chen, Suwon Shon, Hung-yi Lee, Karen Livescu, Shinji Watanabe
| Challenge: | Spoken language understanding evaluation (SLUE) benchmarks are used to benchmark complex spoken language understanding tasks on natural speech. |
| Approach: | They propose a set of benchmark tasks to evaluate spoken language understanding on natural speech . they use pre-trained speech foundation models to evaluate the utility of different SFMs . |
| Outcome: | The proposed framework outperforms pre-trained speech foundation models on natural speech . the proposed framework also outperformed self-supervised SFMs on the sequence generation tasks . |