Automating Template Creation for Ranking-Based Dialogue Models

no code implementations • WS 2020 • Jingxiang Chen, Heba Elfardy, Simi Wang, Andrea Kahn, Jared Kramer

We compare this method to a random baseline that randomly assigns templates to clusters as well as a strong baseline that performs the sentence encoding and the utterance clustering sequentially.

Clustering Response Generation +1

Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting

no code implementations • NAACL 2019 • Yichao Lu, Manisha Srivastava, Jared Kramer, Heba Elfardy, Andrea Kahn, Song Wang, Vikas Bhardwaj

To test our models, a customer service agent handles live contacts and at each turn we present the top four model responses and allow the agent to select (and optionally edit) one of the suggestions or to type their own.

Response Generation

Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification

no code implementations • IJCNLP 2017 • Heba Elfardy, Manisha Srivastava, Wei Xiao, Jared Kramer, Tarun Agarwal

The ability to automatically and accurately process customer feedback is a necessity in the private sector.

Feature Engineering General Classification +5

Improvement of a Naive Bayes Sentiment Classifier Using MRS-Based Features

no code implementations • SEMEVAL 2014 • Jared Kramer, Clara Gordon

Sentiment Analysis