Papers by Sriram Venkatapathy
Adversarial Robustness for Large Language NER models using Disentanglement and Word Attributions (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) tasks are becoming more challenging due to the introduction of complex tagsets, which often leads to the failure of existing NER systems in accurately recognizing these entities. |
| Approach: | They propose a novel attack which relies on disentanglement and word attribution techniques to learn an embedding and identifying important words across both components. |
| Outcome: | The proposed approach improves the F1 score over the original LLM model by 8% and 18% on CoNLL-2003 and Ontonotes 5.0 datasets respectively. |
Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to syntactically controlled paraphrase generation require annotated paraphrase pairs for training and are costly to extend to new domains. |
| Approach: | They propose to leverage Abstract Meaning Representations (AMR) to improve the performance of unsupervised syntactically controlled paraphrase generation. |
| Outcome: | The proposed model generates more accurate syntactically controlled paraphrases, both quantitatively and qualitatively, compared to the existing unsupervised approaches. |
Improving Large-Scale Conversational Assistants using Model Interpretation based Training Sample Selection (2022.emnlp-industry)
Copied to clipboard
Stefan Schroedl, Manoj Kumar, Kiana Hajebi, Morteza Ziyadi, Sriram Venkatapathy, Anil Ramakrishna, Rahul Gupta, Pradeep Natarajan
| Challenge: | Large-scale, voice-based conversational assistants process each utterance through a multi-stage pipeline that includes wakeword detection, automatic speech recognition (ASR), natural language understanding (NLU), entity resolution, and textto-speech. |
| Approach: | They propose a method to identify customer implicitly satisfied with Alexa's responses by leveraging interpretations of model behavior. |
| Outcome: | The proposed approach produces statistically significant improvements in both offline and online tests. |