Zeynab Raeesy
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View article: Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models Open
Personalization, the ability to tailor a system to individual users, is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to lev…
View article: Visual Item Selection With Voice Assistants
Visual Item Selection With Voice Assistants Open
Interacting with voice assistants, such as Amazon Alexa to aid in day-to-day tasks has become a ubiquitous phenomenon in modern-day households. These voice assistants often have screens to provide visual content (e.g., images, videos) to t…
View article: Learning to Retrieve Engaging Follow-Up Queries
Learning to Retrieve Engaging Follow-Up Queries Open
Open domain conversational agents can answer a broad range of targeted queries. However, the sequential nature of interaction with these systems makes knowledge exploration a lengthy task which burdens the user with asking a chain of well …
View article: Learning to Retrieve Engaging Follow-Up Queries
Learning to Retrieve Engaging Follow-Up Queries Open
Open domain conversational agents can answer a broad range of targeted queries. However, the sequential nature of interaction with these systems makes knowledge exploration a lengthy task which burdens the user with asking a chain of well …
View article: Unified Contextual Query Rewriting
Unified Contextual Query Rewriting Open
Yingxue Zhou, Jie Hao, Mukund Rungta, Yang Liu, Eunah Cho, Xing Fan, Yanbin Lu, Vishal Vasudevan, Kellen Gillespie, Zeynab Raeesy. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry …
View article: Multimodal Context Carryover
Multimodal Context Carryover Open
Prashan Wanigasekara, Nalin Gupta, Fan Yang, Emre Barut, Zeynab Raeesy, Kechen Qin, Stephen Rawls, Xinyue Liu, Chengwei Su, Spurthi Sandiri. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry T…
View article: REDAT: Accent-Invariant Representation for End-to-End ASR by Domain Adversarial Training with Relabeling
REDAT: Accent-Invariant Representation for End-to-End ASR by Domain Adversarial Training with Relabeling Open
Accents mismatching is a critical problem for end-to-end ASR. This paper aims to address this problem by building an accent-robust RNN-T system with domain adversarial training (DAT). We unveil the magic behind DAT and provide, for the fir…
View article: Streaming End-to-End Bilingual ASR Systems with Joint Language\n Identification
Streaming End-to-End Bilingual ASR Systems with Joint Language\n Identification Open
Multilingual ASR technology simplifies model training and deployment, but its\naccuracy is known to depend on the availability of language information at\nruntime. Since language identity is seldom known beforehand in real-world\nscenarios…
View article: Streaming End-to-End Bilingual ASR Systems with Joint Language Identification
Streaming End-to-End Bilingual ASR Systems with Joint Language Identification Open
Multilingual ASR technology simplifies model training and deployment, but its accuracy is known to depend on the availability of language information at runtime. Since language identity is seldom known beforehand in real-world scenarios, i…
View article: Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses
Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses Open
This paper presents our modeling and architecture approaches for building a highly accurate low-latency language identification system to support multilingual spoken queries for voice assistants. A common approach to solve multilingual spe…
View article: LSTM-based Whisper Detection
LSTM-based Whisper Detection Open
This article presents a whisper speech detector in the far-field domain. The proposed system consists of a long-short term memory (LSTM) neural network trained on log-filterbank energy (LFBE) acoustic features. This model is trained and ev…