Search Results for author: Srinivas Bangalore

Found 24 papers, 3 papers with code

DialogActs based Search and Retrieval for Response Generation in Conversation Systems

no code implementations ICON 2021 Nidhi Arora, Rashmi Prasad, Srinivas Bangalore

Designing robust conversation systems with great customer experience requires a team of design experts to think of all probable ways a customer can interact with the system and then author responses for each use case individually.

Response Generation Retrieval

1SPU: 1-step Speech Processing Unit

no code implementations8 Nov 2023 Karan Singla, Shahab Jalalvand, Yeon-Jun Kim, Antonio Moreno Daniel, Srinivas Bangalore, Andrej Ljolje, Ben Stern

Recent studies have made some progress in refining end-to-end (E2E) speech recognition encoders by applying Connectionist Temporal Classification (CTC) loss to enhance named entity recognition within transcriptions.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Trustera: A Live Conversation Redaction System

no code implementations16 Mar 2023 Evandro Gouvêa, Ali Dadgar, Shahab Jalalvand, Rathi Chengalvarayan, Badrinath Jayakumar, Ryan Price, Nicholas Ruiz, Jennifer McGovern, Srinivas Bangalore, Ben Stern

Trustera, the first functional system that redacts personally identifiable information (PII) in real-time spoken conversations to remove agents' need to hear sensitive information while preserving the naturalness of live customer-agent conversations.

Automatic Speech Recognition Natural Language Understanding +2

E2E Spoken Entity Extraction for Virtual Agents

no code implementations16 Feb 2023 Karan Singla, Yeon-Jun Kim, Srinivas Bangalore

In human-computer conversations, extracting entities such as names, street addresses and email addresses from speech is a challenging task.

Unsupervised Spoken Utterance Classification

no code implementations2 Jul 2021 Shahab Jalalvand, Srinivas Bangalore

An intelligent virtual assistant (IVA) enables effortless conversations in call routing through spoken utterance classification (SUC) which is a special form of spoken language understanding (SLU).

Classification Sentence +3

Intent Features for Rich Natural Language Understanding

1 code implementation NAACL 2021 Brian Lester, Sagnik Ray Choudhury, Rashmi Prasad, Srinivas Bangalore

Complex natural language understanding modules in dialog systems have a richer understanding of user utterances, and thus are critical in providing a better user experience.

Natural Language Understanding

Multiple Word Embeddings for Increased Diversity of Representation

1 code implementation30 Sep 2020 Brian Lester, Daniel Pressel, Amy Hemmeter, Sagnik Ray Choudhury, Srinivas Bangalore

Most state-of-the-art models in natural language processing (NLP) are neural models built on top of large, pre-trained, contextual language models that generate representations of words in context and are fine-tuned for the task at hand.

Word Embeddings

Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation

no code implementations11 May 2018 Nicholas Ruiz, Srinivas Bangalore, John Chen

With the resurgence of chat-based dialog systems in consumer and enterprise applications, there has been much success in developing data-driven and rule-based natural language models to understand human intent.

Machine Translation Translation

Underspecification in Natural Language Understanding for Dialog Automation

no code implementations RANLP 2017 John Chen, Srinivas Bangalore

With the increasing number of communication platforms that offer variety of ways of connecting two interlocutors, there is a resurgence of chat-based dialog systems.

Natural Language Understanding Speech Recognition

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