Search Results for author:
Found 24 papers, 3 papers with code
DialogActs based Search and Retrieval for Response Generation in Conversation Systems
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.
1SPU: 1-step Speech Processing Unit
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
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
In human-computer conversations, extracting entities such as names, street addresses and email addresses from speech is a challenging task.
Cross-stitched Multi-modal Encoders
In this paper, we propose a novel architecture for multi-modal speech and text input.
Seq-2-Seq based Refinement of ASR Output for Spoken Name Capture
Person name capture from human speech is a difficult task in human-machine conversations.
Unsupervised Spoken Utterance Classification
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).
A Hybrid Approach to Scalable and Robust Spoken Language Understanding in Enterprise Virtual Agents
Spoken language understanding (SLU) extracts the intended mean- ing from a user utterance and is a critical component of conversational virtual agents.
Intent Features for Rich Natural Language Understanding
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.
Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers
Current state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer.
Multiple Word Embeddings for Increased Diversity of Representation
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.
The SLT-Interactions Parsing System at the CoNLL 2018 Shared Task
While segmentation is learned separately, we use neural stacking for joint learning of POS tagging and parsing tasks.
Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation
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.
Underspecification in Natural Language Understanding for Dialog Automation
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.
