5 citations · 6 across the 7 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…
Extracting Similar Questions From Naturally-occurring Business Conversations
Xiliang Zhu, David Rossouw, Shayna Gardiner +1
Pre-trained contextualized embedding models such as BERT are a standard building block in many natural language processing systems. We demonstrate that the sentence-level represent…
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…