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When and Why does a Model Fail? A Human-in-the-loop Error Detection Framework for Sentiment Analysis
Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment.
When and Why a Model Fails? A Human-in-the-loop Error Detection Framework for Sentiment Analysis
Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment.
Accountable Error Characterization
Customers of machine learning systems demand accountability from the companies employing these algorithms for various prediction tasks.
Teacher-Student Learning Paradigm for Tri-training: An Efficient Method for Unlabeled Data Exploitation
Given that labeled data is expensive to obtain in real-world scenarios, many semi-supervised algorithms have explored the task of exploitation of unlabeled data.
Using Structured Representation and Data: A Hybrid Model for Negation and Sentiment in Customer Service Conversations
We further investigate the results of negation scope detection for the sentiment prediction task on customer service conversation data using both a traditional SVM and a Neural Network.
Characterizing machine learning process: A maturity framework
For example, existing machine learning processes cannot address how to define business use cases for an AI application, how to convert business requirements from offering managers into data requirements for data scientists, and how to continuously improve AI applications in term of accuracy and fairness, and how to customize general purpose machine learning models with industry, domain, and use case specific data to make them more accurate for specific situations etc.
Don't get Lost in Negation: An Effective Negation Handled Dialogue Acts Prediction Algorithm for Twitter Customer Service Conversations
Lately, there have been several works proposing a novel taxonomy for fine-grained dialogue acts as well as develop algorithms for automatic detection of these acts.
"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts
We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries.
25 Tweets to Know You: A New Model to Predict Personality with Social Media
Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media.
Fostering User Engagement: Rhetorical Devices for Applause Generation Learnt from TED Talks
One problem that every presenter faces when delivering a public discourse is how to hold the listeners' attentions or to keep them involved.
Home Location Identification of Twitter Users
We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone or geographic region, using the content of users tweets and their tweeting behavior.
Why Are You More Engaged? Predicting Social Engagement from Word Use
We present a study to analyze how word use can predict social engagement behaviors such as replies and retweets in Twitter.
