Related papers: A Transfer Learning Approach for Dialogue Act Clas…
Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue. Existing approaches to DA classification model utterances without incorporating the turn changes among speakers…
Pre-trained models have achieved excellent performance on the dialogue task. However, for the continual increase of online chit-chat scenarios, directly fine-tuning these models for each of the new tasks not only explodes the capacity of…
Most existing methods focus on sentiment analysis of textual data. However, recently there has been a massive use of images and videos on social platforms, motivating sentiment analysis from other modalities. Current studies show that…
Topic models are widely used in natural language processing, allowing researchers to estimate the underlying themes in a collection of documents. Most topic models use unsupervised methods and hence require the additional step of attaching…
The increasing volume of short texts generated on social media sites, such as Twitter or Facebook, creates a great demand for effective and efficient topic modeling approaches. While latent Dirichlet allocation (LDA) can be applied, it is…
Project based learning (PBL) for software development (we call it software development PBL) has garnered attention as a practical educational method. A number of studies have reported on the introduction of social coding tools such as…
A set of steps for implementing a chatbot, to support decision-making activities in the software incident management process is proposed and discussed in this article. Each step is presented independently of the platform used for the…
Automatic evaluation of open-domain dialogue response generation is very challenging because there are many appropriate responses for a given context. Existing evaluation models merely compare the generated response with the ground truth…
The ultimate goal of transfer learning is to reduce labeled data requirements by exploiting a pre-existing embedding model trained for different datasets or tasks. The visual and language communities have established benchmarks to compare…
With the growth of social medias, such as Twitter, plenty of user-generated data emerge daily. The short texts published on Twitter -- the tweets -- have earned significant attention as a rich source of information to guide many…
We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of…
Building user trust in dialogue agents requires smooth and consistent dialogue exchanges. However, agents can easily lose conversational context and generate irrelevant utterances. These situations are called dialogue breakdown, where agent…
Improving user experience of a dialogue system often requires intensive developer effort to read conversation logs, run statistical analyses, and intuit the relative importance of system shortcomings. This paper presents a novel approach to…
Recent advances in natural-language processing and data analysis allow software bots to become virtual team members, providing an additional set of automated eyes and additional perspectives for informing and supporting teamwork. In this…
Software development projects rely on issue tracking systems at the core of tracking maintenance tasks such as bug reports, and enhancement requests. Incoming issue-reports on these issue tracking systems must be managed in an effective…
GitHub is the world's largest host of source code, with more than 150M repositories. However, most of these repositories are not labeled or inadequately so, making it harder for users to find relevant projects. There have been various…
A semantic equivalence assessment is defined as a task that assesses semantic equivalence in a sentence pair by binary judgment (i.e., paraphrase identification) or grading (i.e., semantic textual similarity measurement). It constitutes a…
Sentiment analysis, an increasingly vital field in both academia and industry, plays a pivotal role in machine learning applications, particularly on social media platforms like Reddit. However, the efficacy of sentiment analysis models is…
Large language models (LLMs) like ChatGPT have shown the potential to assist developers with coding and debugging tasks. However, their role in collaborative issue resolution is underexplored. In this study, we analyzed 1,152…
Building multi-turn information-seeking conversation systems is an important and challenging research topic. Although several advanced neural text matching models have been proposed for this task, they are generally not efficient for…