Papers by Amita Misra
SlugNERDS: A Named Entity Recognition Tool for Open Domain Dialogue Systems (L18-1)
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| Challenge: | UCSC researchers have developed an open domain social bot aimed at casual conversation . NER and NEL are important preprocessing steps for understanding user intent in open domain dialogue systems. |
| Approach: | They propose a tool for NER and NEL in open domain dialogue that addresses these challenges . they also propose two corpora based on 10,000 real user conversations . |
| Outcome: | The proposed open domain social bot is aimed at casual conversation. |
ProMISe: A Proactive Multi-turn Dialogue Dataset for Information-seeking Intent Resolution (2024.findings-eacl)
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| Challenge: | Work done during internship at Amazon Alexa AI. |
| Approach: | They propose to use iterative suggested question-answering conversation to improve the trade-off between satisfaction of the user’s intent and keeping the information exchange natural. |
| Outcome: | The proposed proposed question-answering conversation improves the satisfaction of the user’s intent while keeping the information exchange natural and cognitive load of the interaction minimal on the users. |
Towards Improved Multi-Source Attribution for Long-Form Answer Generation (2024.naacl-long)
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| Challenge: | Current LLMs struggle with attribution for long-form answers which require reasoning over multiple evidence sources. |
| Approach: | They propose to improve attribution capability of large language models for long-form answer generation to multiple sources with multiple citations per sentence. |
| Outcome: | The proposed model improves on a wide range of attribution benchmark datasets on PolitiICite, a multi-source attribution dataset based on PolitIcite articles . |
Controlled Text Generation with Hidden Representation Transformations (2023.findings-acl)
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| Challenge: | Using a con-trolled language model, we gain attribute control by modifying the hidden representation of thebase model through learning transformations. |
| Approach: | They propose a con-trolled language generation framework that gains attribute control bymodifying the hidden representation of thebase model through learned transformations. |
| Outcome: | The proposed framework outperforms all thebaselines in detoxification, positivesentiment steering, and text simplification while minimizing the loss in linguistic qualities. |
Machine translation impact in E-commerce multilingual search (2022.emnlp-industry)
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| Challenge: | Existing studies have demonstrated that cross-lingual information retrieval performance is highly dependent on query translation quality. |
| Approach: | They investigate whether improving query translation quality yields little or no benefit to further improve retrieval performance. |
| Outcome: | The proposed methods compare query translations for multiple language pairs and identify the most promising language pairs to invest and improve. |
APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI (2026.acl-long)
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| Challenge: | Large language models struggle with reliable long-term conversational memory . enlarging context windows or applying nave retrieval often introduces noise . |
| Approach: | They propose a conversational memory system that uses domain-agnostic ontology to structure conversations as temporally grounded events in an entity-centric framework. |
| Outcome: | APEX-MEM outperforms state-of-the-art retrieval methods in accuracy and time resolution. |