5 citations · 6 across the 5 of their papers we have counts for
5 papers · 1 filter
An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts
Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar +2
We present a simple yet effective method to train a named entity recognition (NER) model that operates on business telephone conversation transcripts that contain noise due to the…
Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning
Xiliang Zhu, Shayna Gardiner, David Rossouw +2
Automatic Speech Recognition (ASR) systems typically produce unpunctuated transcripts that have poor readability. In addition, building a punctuation restoration system is challeng…
Developing a Production System for Purpose of Call Detection in Business Phone Conversations
Elena Khasanova, Pooja Hiranandani, Shayna Gardiner +3
For agents at a contact centre receiving calls, the most important piece of information is the reason for a given call. An agent cannot provide support on a call if they do not kno…
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations
Md Tahmid Rahman Laskar, Cheng Chen, Aliaksandr Martsinovich +4
An Entity Linking system aligns the textual mentions of entities in a text to their corresponding entries in a knowledge base. However, deploying a neural entity linking system for…
Improving Punctuation Restoration for Speech Transcripts via External Data
Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar +2
Automatic Speech Recognition (ASR) systems generally do not produce punctuated transcripts. To make transcripts more readable and follow the expected input format for downstream la…